The effect of a dominant shrub on annuals plants in a desert system changes along a gradient of precipitation.
Alex Filazzola
May 2017
Ephedra regional gradient (ERG) analyses
Abstract
An under-examined component of the shrub-annual relationship is how regional drivers, such as climate, may alter the sign or magnitude of positive interactions. Interspecific interactions between plants have been shown to be strongly linked to climate, particularly temperature and precipitation. The stress-gradient hypothesis (SGH) predicts that higher abiotic stress (i.e. for deserts, increasing temperature and reduced precipitation) will increase the frequency of positive interactions among shrubs and their annual understory. Regional climate gradients also have indirect effects on plant composition, such as determining consumer abundance and soil nutrient composition. Nutrient availability is particularly affected by precipitation because of altered decomposition rates of organic matter and mineralization. Therefore, the strength of facilitation and operating mechanism of a shrub on the annual plant community may change along a regional gradient.
Hypothesis
We tested the hypothesis that positive interactions among shrubs and annual plants will increase with abiotic stress and reduce nutrient availability along a regional gradient of aridity.
Methods
Weather for growing season
Climate patterns within study
season1.sjd <- season1 %>% filter(Gradient<4) %>% group_by(year, month,days) %>% summarise_if(is.numeric, funs(mean(., na.rm=T)))
season1.mnp <- season1 %>% filter(Gradient>3)%>% group_by(year, month,days) %>% summarise_if(is.numeric, funs(mean(., na.rm=T)))
season2.sjd <- season2 %>% filter(Gradient<4) %>% group_by(year, month,days) %>% summarise_if(is.numeric, funs(mean(., na.rm=T)))
season2.mnp <- season2 %>% filter(Gradient>3)%>% group_by(year, month,days) %>% summarise_if(is.numeric, funs(mean(., na.rm=T)))
## Rain vs Temperature in 2016
par(mfrow=c(2,1))
par(mar=c(3.5,4.5,1,4.5))
plot1 <- barplot(height=season1.sjd$Precip, ylim=c(0,14), ylab="Average precipitation San Joaquin (cm)")
points(plot1[,1], season1.sjd$min.temp, type="l", col="#FF000050", lwd=2)
axis(4, at=seq(0,14,2), lab=seq(0,14,2), ylab="")
mtext("Average temperature at all sites (°C)", 4, line=3)
par(mar=c(4.5,4.5,0,4.5))
plot1 <- barplot(height=season1.mnp$Precip, ylim=c(0,14), ylab="Average precipitation Mojave (cm)")
axis(1, plot1[c(1,30,60,90,120,150,180)], c("Nov","Dec","Jan","Feb","Mar","Apr","May"))
points(plot1[,1], season1.mnp$min.temp, type="l", col="#FF000050", lwd=2)
axis(4, at=seq(0,14,2), lab=seq(0,14,2), ylab="")
mtext("Average temperature at all sites (°C)", 4, line=3)
## Rain vs Temperature in 2017
par(mfrow=c(2,1))
par(mar=c(3.5,4.5,1,4.5))
plot2 <- barplot(height=season2.sjd$Precip, ylim=c(0,14), ylab="Average precipitation San Joaquin (cm)")
points(plot2[,1], season2.sjd$min.temp, type="l", col="#FF000050", lwd=2)
axis(4, at=seq(0,14,2), lab=seq(0,14,2), ylab="")
mtext("Average temperature San Joaquin (°C)", 4, line=3)
par(mar=c(4.5,4.5,0,4.5))
plot2 <- barplot(height=season2.mnp$Precip, ylim=c(0,14), ylab="Average precipitation Mojave (cm)")
points(plot2[,1], season2.mnp$min.temp, type="l", col="#FF000050", lwd=2)
axis(1, plot1[c(1,30,60,90,120,150,180)], c("Nov","Dec","Jan","Feb","Mar","Apr","May"))
axis(4, at=seq(0,14,2), lab=seq(0,14,2), ylab="")
mtext("Average temperature Mojave (°C)", 4, line=3)
### 2016 The rain was inconsistent and mostly absent in the Mojave. This resulted in low germination and producitivty at the southern sites
### 2017 The rain was more plentiful, but in the northern sites, there appears to be a frost period after the majority of the rainfall. Need to check number of frost days
season1.frost <- season1 %>% group_by(Site) %>% summarize(frost.days=sum(min.temp<0, na.rm=T)/length(min.temp)*100)
data.frame(season1.frost)## Site frost.days
## 1 Barstow 24.725275
## 2 Cuyama 29.670330
## 3 HeartofMojave 25.824176
## 4 PanocheHills 10.439560
## 5 SheepholeValley 6.043956
## 6 Tecopa 23.626374
## 7 TejonRanch 50.549451season2.frost <- season2 %>% group_by(Site) %>% summarize(frost.days=sum(min.temp<0, na.rm=T)/length(min.temp)*100)
data.frame(season2.frost)## Site frost.days
## 1 Barstow 17.679558
## 2 Cuyama 19.889503
## 3 HeartofMojave 16.574586
## 4 PanocheHills 14.917127
## 5 SheepholeValley 1.785714
## 6 Tecopa 25.966851
## 7 TejonRanch 35.911602## Both years had comparable number of frost days
## Compare number of consecutive frost days (i.e. frost periods)
season1[,"frost"] <- ifelse(season1$min.temp<0, -99,season1$min.temp) ## identified days below freezing
season2[,"frost"] <- ifelse(season2$min.temp<0, -99,season2$min.temp) ## identified days below freezing
count.consec <- function(x) {max(rle(as.character(x))$lengths)}
season1.frost <- season1 %>% group_by(Site) %>% summarize(count.consec(frost))
data.frame(season1.frost)## Site count.consec.frost.
## 1 Barstow 10
## 2 Cuyama 10
## 3 HeartofMojave 12
## 4 PanocheHills 5
## 5 SheepholeValley 7
## 6 Tecopa 11
## 7 TejonRanch 14season2.frost <- season2 %>% group_by(Site) %>% summarize(count.consec(frost))
data.frame(season2.frost)## Site count.consec.frost.
## 1 Barstow 5
## 2 Cuyama 6
## 3 HeartofMojave 7
## 4 PanocheHills 6
## 5 SheepholeValley 3
## 6 Tecopa 7
## 7 TejonRanch 13## compare only after plants have germinated
season1.frost <- season1 %>% group_by(Site) %>% filter(year>2015) %>% summarize(frost.days=sum(min.temp<0, na.rm=T)/length(min.temp)*100, avg.min.temp=mean(min.temp, na.rm=T))
data.frame(season1.frost)## Site frost.days avg.min.temp
## 1 Barstow 12.396694 5.750413
## 2 Cuyama 11.570248 3.352893
## 3 HeartofMojave 16.528926 4.542149
## 4 PanocheHills 2.479339 6.777686
## 5 SheepholeValley 2.479339 9.394167
## 6 Tecopa 11.570248 6.342149
## 7 TejonRanch 33.884298 1.438017season2.frost <- season2 %>% group_by(Site) %>% filter(year>2016) %>% summarize(frost.days=sum(min.temp<0, na.rm=T)/length(min.temp)*100,avg.min.temp=mean(min.temp, na.rm=T))
data.frame(season2.frost)## Site frost.days avg.min.temp
## 1 Barstow 11.666667 6.052500
## 2 Cuyama 16.666667 3.624167
## 3 HeartofMojave 8.333333 5.637838
## 4 PanocheHills 8.333333 6.065833
## 5 SheepholeValley 0.000000 9.386916
## 6 Tecopa 20.833333 5.938333
## 7 TejonRanch 28.333333 2.535833season1.frost <- season1 %>% group_by(Site) %>% filter(year>2015) %>% summarize(count.consec(frost))
data.frame(season1.frost)## Site count.consec.frost.
## 1 Barstow 5
## 2 Cuyama 6
## 3 HeartofMojave 4
## 4 PanocheHills 2
## 5 SheepholeValley 2
## 6 Tecopa 4
## 7 TejonRanch 10season2.frost <- season2 %>% group_by(Site) %>% filter(year>2016)%>% summarize(count.consec(frost))
data.frame(season2.frost)## Site count.consec.frost.
## 1 Barstow 5
## 2 Cuyama 6
## 3 HeartofMojave 3
## 4 PanocheHills 3
## 5 SheepholeValley 2
## 6 Tecopa 5
## 7 TejonRanch 10Microenvironmental differences
We compared temperature and relative humidity between shrub and open microsites among all sites along the regional gradient 
Shapiro-Wilk normality test
data: fit1$residuals
W = 0.87899, p-value < 2.2e-16 Df Sum Sq Mean Sq F value Pr(>F)
Microsite 1 33.6 33.59 76.013 <2e-16 ***
gradient 6 234.0 39.00 88.248 <2e-16 ***
Microsite:gradient 6 8.8 1.46 3.306 0.003 **
Residuals 4290 1896.0 0.44
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
30 observations deleted due to missingnessAnalysis of Deviance Table
Model: binomial, link: logit
Response: RH/100
Terms added sequentially (first to last)
Df Deviance Resid. Df Resid. Dev Pr(>Chi)
NULL 4333 974.99
Microsite 1 2.11 4332 972.88 0.1463
gradient 6 528.28 4326 444.60 <2e-16 ***
Microsite:gradient 6 7.44 4320 437.17 0.2824
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Analysis of Deviance Table
Model: Gamma, link: inverse
Response: swc
Terms added sequentially (first to last)
Df Deviance Resid. Df Resid. Dev F Pr(>F)
NULL 404 231.405
Site 6 189.683 398 41.722 381.2179 <2e-16 ***
Microsite 1 0.287 397 41.435 3.4579 0.0637 .
Site:Microsite 6 0.696 391 40.739 1.3994 0.2136
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Analysis of Deviance Table
Model: Gamma, link: inverse
Response: swc
Terms added sequentially (first to last)
Df Deviance Resid. Df Resid. Dev F Pr(>F)
NULL 419 183.193
Site 6 144.930 413 38.263 279.4537 <2e-16 ***
Microsite 1 0.080 412 38.183 0.9284 0.3358
Site:Microsite 6 0.535 406 37.647 1.0322 0.4037
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
site level means
## site coordintes
site.gps <- read.csv("Data/ERGsites.csv")
# nutrient data for each site
nutrients <- read.csv("Data/ERG.soilnutrients.csv")
## long term climate data extracted from worldclim
clim.dat <- read.csv("Data/ERG.worldclim.csv")
## check correlation among long-term climate variables
cor(clim.dat[,3:8]) ## Temp.wettest.QR least correlated ## Annual.Temp Temp.Seasonality Temp.wettest.QR
## Annual.Temp 1.0000000 0.9051791 0.7260617
## Temp.Seasonality 0.9051791 1.0000000 0.4650548
## Temp.wettest.QR 0.7260617 0.4650548 1.0000000
## Annual.precip -0.8911069 -0.9650533 -0.5313460
## Precip.seasonality -0.9715419 -0.9774646 -0.6165170
## Precipt.wettest.QR -0.8839124 -0.9570957 -0.5523347
## Annual.precip Precip.seasonality Precipt.wettest.QR
## Annual.Temp -0.8911069 -0.9715419 -0.8839124
## Temp.Seasonality -0.9650533 -0.9774646 -0.9570957
## Temp.wettest.QR -0.5313460 -0.6165170 -0.5523347
## Annual.precip 1.0000000 0.9613729 0.9980959
## Precip.seasonality 0.9613729 1.0000000 0.9543657
## Precipt.wettest.QR 0.9980959 0.9543657 1.0000000## Obtain mean shrub traits for each site
shrubs <- read.csv("Data/ERG.shrub.csv")
shrubs <- subset(shrubs, Microsite=="shrub")
shrubs.mean <- shrubs %>% group_by(Gradient,Site) %>% summarise_all(funs(mean))
shrubs.mean <- data.frame(shrubs.mean)
shrubs.vars <- shrubs.mean[,c("Site","Gradient","volume","canopy","Dx","DxEph","Compaction")]
## test correlation among shrub traits
cor(shrubs.vars[,3:7]) ## compaction x volume & DxEph x Dx high correlation## volume canopy Dx DxEph Compaction
## volume 1.0000000 0.2243902 0.4003665 0.2631563 -0.7023062
## canopy 0.2243902 1.0000000 -0.6847957 -0.3538094 -0.1437590
## Dx 0.4003665 -0.6847957 1.0000000 0.7803527 -0.1355700
## DxEph 0.2631563 -0.3538094 0.7803527 1.0000000 0.3221944
## Compaction -0.7023062 -0.1437590 -0.1355700 0.3221944 1.0000000vifstep(shrubs.vars[,3:7], th=10) ## Dx showing collinearity problems## 1 variables from the 5 input variables have collinearity problem:
##
## Dx
##
## After excluding the collinear variables, the linear correlation coefficients ranges between:
## min correlation ( Compaction ~ canopy ): -0.143759
## max correlation ( Compaction ~ volume ): -0.7023062
##
## ---------- VIFs of the remained variables --------
## Variables VIF
## 1 volume 7.026144
## 2 canopy 1.925499
## 3 DxEph 4.316058
## 4 Compaction 6.400914shrub.site <- shrubs.vars[,-c(1,2,5)]
rownames(shrub.site) <- shrubs.vars[,1]
## PCA of shrub characteristics
pca1 <- prcomp(log(shrub.site), scale=T)
plot(pca1)
biplot(pca1, scale = T)
summary(pca1) ## 77% variation explained## Importance of components:
## PC1 PC2 PC3 PC4
## Standard deviation 1.3017 1.1778 0.9082 0.3059
## Proportion of Variance 0.4236 0.3468 0.2062 0.0234
## Cumulative Proportion 0.4236 0.7704 0.9766 1.0000## PCA of site characteristics for season1
## Obtain mean weather variables for each site
season1.mean <- season1 %>% group_by(Gradient,Site) %>% summarise(temp.var=var(avg.temp,na.rm=T),Precip=sum(Precip),wind=mean(wind.speed, na.rm=T))
season1.mean <- data.frame(season1.mean)
## extract key variables
## dropped RH, and chose min.temp because least correlation with others & cold stress
##combine nutrients and long-term averages
site.vars <- data.frame(season1.mean[,3:5],site.gps["elevation"]) ## drop Phosphorus and other climate variables because of correlations,
row.names(site.vars) <- shrubs.vars[,1]
cor(site.vars)## temp.var Precip wind elevation
## temp.var 1.00000000 -0.8679054 0.05816084 -0.2699588
## Precip -0.86790537 1.0000000 -0.37279135 0.1222504
## wind 0.05816084 -0.3727914 1.00000000 0.1054497
## elevation -0.26995879 0.1222504 0.10544974 1.0000000site.vars2016 <- site.vars
## check for collinearity
vifstep(site.vars, th=10) ## remove potassium and temperature minimum## No variable from the 4 input variables has collinearity problem.
##
## The linear correlation coefficients ranges between:
## min correlation ( wind ~ temp.var ): 0.05816084
## max correlation ( Precip ~ temp.var ): -0.8679054
##
## ---------- VIFs of the remained variables --------
## Variables VIF
## 1 temp.var 6.640710
## 2 Precip 7.316232
## 3 wind 1.739558
## 4 elevation 1.142681pca1 <- prcomp(log(abs(site.vars)), scale=T)
plot(pca1)
biplot(pca1)
## check contribution of loadings
pca1$rotation## PC1 PC2 PC3 PC4
## temp.var 0.6001002 -0.1669018 0.50554765 -0.5970303
## Precip -0.6703313 -0.1105034 -0.09936844 -0.7270288
## wind 0.2743589 0.7981791 -0.43410209 -0.3149488
## elevation -0.3395039 0.5681927 0.73898773 0.1256633aload <- abs(pca1$rotation)
sweep(aload, 2, colSums(aload), "/")## PC1 PC2 PC3 PC4
## temp.var 0.3184748 0.1015355 0.28433406 0.33832382
## Precip 0.3557466 0.0672253 0.05588758 0.41199111
## wind 0.1456030 0.4855763 0.24415109 0.17847449
## elevation 0.1801756 0.3456629 0.41562726 0.07121058summary(pca1) ## 89% variation explained## Importance of components:
## PC1 PC2 PC3 PC4
## Standard deviation 1.456 1.0458 0.8577 0.22479
## Proportion of Variance 0.530 0.2734 0.1839 0.01263
## Cumulative Proportion 0.530 0.8034 0.9874 1.00000gradient1.season1 <- pca1$x[,1]
gradient2.season1 <- pca1$x[,2]
## season2
season2.mean <- season2 %>% group_by(Gradient,Site) %>% summarise(temp.var=var(avg.temp,na.rm=T),Precip=sum(Precip, na.rm=T),wind=mean(wind.speed, na.rm=T))
season2.mean <- data.frame(season2.mean)
## extract key variables
## dropped RH, and chose min.temp because least correlation with others & cold stress
##combine nutrients and long-term averages
site.vars <- data.frame(season2.mean[,3:5],site.gps["elevation"]) ## drop Phosphorus and other climate variables because of correlations,
row.names(site.vars) <- shrubs.vars[,1]
cor(site.vars)## temp.var Precip wind elevation
## temp.var 1.0000000 -0.7427028 -0.2262409 -0.3715471
## Precip -0.7427028 1.0000000 -0.1551517 0.1813600
## wind -0.2262409 -0.1551517 1.0000000 0.1052805
## elevation -0.3715471 0.1813600 0.1052805 1.0000000site.vars2017 <- site.vars
## check for collinearity
vifstep(site.vars, th=10) ## remove potassium and temperature minimum## No variable from the 4 input variables has collinearity problem.
##
## The linear correlation coefficients ranges between:
## min correlation ( elevation ~ wind ): 0.1052805
## max correlation ( Precip ~ temp.var ): -0.7427028
##
## ---------- VIFs of the remained variables --------
## Variables VIF
## 1 temp.var 3.438786
## 2 Precip 3.035337
## 3 wind 1.402010
## 4 elevation 1.192010pca2 <- prcomp(log(abs(site.vars)), scale=T)
plot(pca2)
biplot(pca2)
summary(pca2) ## 85% variation explained## Importance of components:
## PC1 PC2 PC3 PC4
## Standard deviation 1.434 1.0121 0.8809 0.37883
## Proportion of Variance 0.514 0.2561 0.1940 0.03588
## Cumulative Proportion 0.514 0.7701 0.9641 1.00000## check contribution of loadings
pca2$rotation## PC1 PC2 PC3 PC4
## temp.var 0.6557532 -0.09123357 0.20565681 0.7206729
## Precip -0.6176537 -0.16891771 -0.40105332 0.6550778
## wind -0.1019884 0.97279862 0.06826009 0.1964733
## elevation -0.4220070 -0.12963831 0.89005734 0.1135866aload <- abs(pca2$rotation)
sweep(aload, 2, colSums(aload), "/")## PC1 PC2 PC3 PC4
## temp.var 0.36483385 0.06695609 0.1314078 0.42749339
## Precip 0.34363685 0.12396828 0.2562596 0.38858328
## wind 0.05674213 0.71393442 0.0436159 0.11654531
## elevation 0.23478717 0.09514122 0.5687167 0.06737801## define gradients
gradient1.season2 <- pca2$x[,1]
gradient2.season2 <- pca2$x[,2]Compare gradient to phytometer
# crs.world <-CRS("+proj=longlat +datum=WGS84")
# gps <- data.frame(x=site.gps$long,y=site.gps$lat)
# coordinates(gps) <- ~x+y
# proj4string(gps) <- crs.world
#
# ## Download and extract PET/aridity
# r1 <- raster("C:\\Users\\Alessandro\\Downloads\\Global Aridity - Annual\\AI_annual\\ai_yr\\w001001.adf", package="raster") #aridity
# r2 <- raster("C:\\Users\\Alessandro\\Downloads\\Global PET - Annual\\PET_he_annual\\pet_he_yr\\w001001.adf", package="raster") #potential evapotranspiration
# calclim <- stack(r1,r2)
# names(calclim) <- c("aridity","PET")
# arid.vals <- raster::extract(calclim, gps)
# rownames(arid.vals) <- site.gps[,"name"]
# colnames(arid.vals)[1] <- "Gradient"
# write.csv(arid.vals, "Data/aridity.PET.csv")
arid.vals <- read.csv("Data/aridity.PET.csv")
colnames(arid.vals)[1] <- "Site"
par(mfrow=c(1,2))
mean.phyto <- census %>% filter(Census=="end") %>%group_by(Year, Gradient, Site, Microsite) %>% summarise_if(is.numeric, funs(mean(., na.rm=T)))
mean.phyto <- data.frame(mean.phyto)
rii.data <- rii(census, 1:6, var=9:15)
rii.mean <- rii.data %>% group_by(Year, Gradient,Site) %>% summarise_if(is.numeric, funs(mean(., na.rm=T)))
rii.mean <- data.frame(rii.mean)
season1.mean <- season1 %>% group_by(Gradient,Site) %>% summarise(temp.var=var(avg.temp,na.rm=T),Precip=sum(Precip),wind=mean(wind.speed, na.rm=T), max.temp=abs(mean(max.temp, na.rm=T)),min.temp=abs(mean(min.temp, na.rm=T)),avg.temp=abs(mean(avg.temp, na.rm=T)))
s1.mean <- data.frame(season1.mean)
season2.mean <- season2 %>% group_by(Gradient,Site) %>% summarise(temp.var=var(avg.temp,na.rm=T),Precip=sum(Precip, na.rm=T),wind=mean(wind.speed, na.rm=T), max.temp=abs(mean(max.temp, na.rm=T)),min.temp=abs(mean(min.temp, na.rm=T)),avg.temp=abs(mean(avg.temp, na.rm=T)))
s2.mean <- data.frame(season2.mean)
s1.mean[,"arid.gradient"] <- log(s1.mean[,"Precip"]/arid.vals[,"PET"])
s2.mean[,"arid.gradient"] <- log(s2.mean[,"Precip"]/(arid.vals[,"PET"]))
## collect aridity gradient data
site.climate <- rbind(s1.mean,s2.mean)
site.climate[,"Year"] <- as.factor(c(rep("2016",7),rep("2017",7)))
## combine climate data with census
mean.phyto <- merge(mean.phyto , site.climate, by=c("Year","Site"))
phyto.2016 <- subset(mean.phyto, Year==2016)
plot(jitter(phyto.2016[phyto.2016$Microsite=="open","arid.gradient"],1), phyto.2016[phyto.2016$Microsite=="open","phyto.biomass"], ylim=c(0,2), ylab=c("all phytometer biomass (g)"), xlab=c("aridity gradient"))
points(jitter(phyto.2016[phyto.2016$Microsite=="open","arid.gradient"],2), phyto.2016[phyto.2016$Microsite=="shrub","phyto.biomass"], pch=19)
legend(-4.5,1.8, c("shrub","open"), pch=c(19,1))
text(-2.3, 1.8, "2016", cex=1.5)
plot(phyto.2016[phyto.2016$Microsite=="open","arid.gradient"], rii.mean[rii.mean$Year==2016,"phyto.biomass"], ylim=c(-0.4,0.4), ylab="RII all phytometer biomass", pch=19, xlab=c("aridity gradient"))
text(-2.3, 0.38, "2016", cex=1.5)
phyto.2017 <- subset(mean.phyto, Year==2017)
plot(jitter(phyto.2017[phyto.2017$Microsite=="open","arid.gradient"],1), phyto.2017[phyto.2017$Microsite=="open","phyto.biomass"], ylim=c(0,3), xlab=c("aridity gradient"),ylab=c("all phytometer biomass (g)"))
points(jitter(phyto.2017[phyto.2017$Microsite=="open","arid.gradient"],2), phyto.2017[phyto.2017$Microsite=="shrub","phyto.biomass"], pch=19)
legend(-4.5,1.8, c("shrub","open"), pch=c(19,1))
text(-2.0, 2.8, "2017", cex=1.5)
plot(phyto.2017[phyto.2017$Microsite=="open","arid.gradient"], rii.mean[rii.mean$Year==2017,"phyto.biomass"], ylim=c(-0.4,0.4), ylab="RII all phytometer biomass", pch=19, xlab=c("aridity gradient"))
text(-2.0, 0.38, "2017", cex=1.5)
## regress gradient and microsite on biomass for phytometers in 2016
m1 <- lm(ihs(Phacelia.biomass)~arid.gradient * Microsite, data=phyto.2016)
summary(m1) ## nothing significant##
## Call:
## lm(formula = ihs(Phacelia.biomass) ~ arid.gradient * Microsite,
## data = phyto.2016)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.26646 -0.16692 0.01821 0.09053 0.43991
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.75896 0.31899 2.379 0.0387 *
## arid.gradient 0.16726 0.09147 1.829 0.0974 .
## Micrositeshrub 0.81930 0.45112 1.816 0.0994 .
## arid.gradient:Micrositeshrub 0.19010 0.12936 1.470 0.1724
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2383 on 10 degrees of freedom
## Multiple R-squared: 0.674, Adjusted R-squared: 0.5762
## F-statistic: 6.893 on 3 and 10 DF, p-value: 0.008498m2 <- lm(ihs(Plantago.biomass)~arid.gradient * Microsite, data=phyto.2016)
summary(m2) ## nothing significant##
## Call:
## lm(formula = ihs(Plantago.biomass) ~ arid.gradient * Microsite,
## data = phyto.2016)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.070995 -0.048467 0.003316 0.024049 0.115472
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.27085 0.07901 3.428 0.00646 **
## arid.gradient 0.05886 0.02266 2.598 0.02658 *
## Micrositeshrub -0.12531 0.11174 -1.121 0.28831
## arid.gradient:Micrositeshrub -0.02928 0.03204 -0.914 0.38239
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.05904 on 10 degrees of freedom
## Multiple R-squared: 0.4794, Adjusted R-squared: 0.3232
## F-statistic: 3.069 on 3 and 10 DF, p-value: 0.07774m3 <- lm(ihs(Salvia.biomass)~arid.gradient * Microsite, data=phyto.2016)
summary(m3)## aridity significant##
## Call:
## lm(formula = ihs(Salvia.biomass) ~ arid.gradient * Microsite,
## data = phyto.2016)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.15051 -0.09099 -0.01568 0.03332 0.26045
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.748316 0.194293 3.851 0.0032 **
## arid.gradient 0.159284 0.055714 2.859 0.0170 *
## Micrositeshrub -0.000770 0.274771 -0.003 0.9978
## arid.gradient:Micrositeshrub 0.004763 0.078792 0.060 0.9530
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1452 on 10 degrees of freedom
## Multiple R-squared: 0.6281, Adjusted R-squared: 0.5165
## F-statistic: 5.63 on 3 and 10 DF, p-value: 0.01599## regress gradient and microsite on biomass for phytometers in 2017
m4 <- lm(ihs(Phacelia.biomass)~arid.gradient * Microsite, data=phyto.2017)
summary(m4) ## nothing significant##
## Call:
## lm(formula = ihs(Phacelia.biomass) ~ arid.gradient * Microsite,
## data = phyto.2017)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.53579 -0.12720 0.00415 0.29889 0.40793
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.725 3.895 -1.727 0.135
## arid.gradient -2.514 1.315 -1.912 0.104
## Micrositeshrub 6.929 4.012 1.727 0.135
## arid.gradient:Micrositeshrub 2.341 1.363 1.718 0.137
##
## Residual standard error: 0.4168 on 6 degrees of freedom
## (4 observations deleted due to missingness)
## Multiple R-squared: 0.3953, Adjusted R-squared: 0.09289
## F-statistic: 1.307 on 3 and 6 DF, p-value: 0.3557m5 <- lm(ihs(Plantago.biomass)~arid.gradient * Microsite, data=phyto.2017)
summary(m5) ## aridity significant##
## Call:
## lm(formula = ihs(Plantago.biomass) ~ arid.gradient * Microsite,
## data = phyto.2017)
##
## Residuals:
## 15 16 19 20 23 24 25
## -0.008390 -0.070144 0.006678 -0.031325 0.126617 0.249998 -0.124905
## 26
## -0.148529
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.4847 1.6331 -0.909 0.415
## arid.gradient -0.6486 0.5513 -1.177 0.305
## Micrositeshrub 3.1150 2.3096 1.349 0.249
## arid.gradient:Micrositeshrub 1.1087 0.7796 1.422 0.228
##
## Residual standard error: 0.1748 on 4 degrees of freedom
## (6 observations deleted due to missingness)
## Multiple R-squared: 0.4909, Adjusted R-squared: 0.109
## F-statistic: 1.285 on 3 and 4 DF, p-value: 0.3935m6 <- lm(ihs(Salvia.biomass)~arid.gradient * Microsite, data=phyto.2017)
summary(m6)## nothing significant##
## Call:
## lm(formula = ihs(Salvia.biomass) ~ arid.gradient * Microsite,
## data = phyto.2017)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.52276 -0.13176 -0.02786 0.05787 0.53706
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.5442 0.6266 -0.869 0.405
## arid.gradient -0.4200 0.2423 -1.733 0.114
## Micrositeshrub 0.2852 0.8861 0.322 0.754
## arid.gradient:Micrositeshrub 0.1488 0.3427 0.434 0.673
##
## Residual standard error: 0.335 on 10 degrees of freedom
## Multiple R-squared: 0.3112, Adjusted R-squared: 0.1046
## F-statistic: 1.506 on 3 and 10 DF, p-value: 0.2723#
# ## Phacelia season 1
# plot(jitter(phyto.2016[phyto.2016$Microsite=="open","arid.gradient"] ,1), phyto.2016[phyto.2016$Microsite=="open","Phacelia.biomass"], ylim=c(0,2))
# points(jitter(phyto.2016[phyto.2016$Microsite=="shrub","arid.gradient"] ,2), phyto.2016[phyto.2016$Microsite=="shrub","Phacelia.biomass"], pch=19)
#
# m1 <- lm(ihs(Phacelia.biomass)~arid.gradient * Microsite, data=phyto.2016)
# m2 <- lm(ihs(Plantago.biomass)~arid.gradient * Microsite, data=phyto.2016)
# m3 <- lm(ihs(Salvia.biomass)~arid.gradient * Microsite, data=phyto.2016)
#
# plot(phyto.2016[phyto.2016$Microsite=="open","arid.gradient"] , rii.mean[rii.mean$Year==2016,"Phacelia.biomass"], ylim=c(-0.4,0.4))
#
# ## Plantago season 1
# plot(jitter(phyto.2016[phyto.2016$Microsite=="open","arid.gradient"] ,1), phyto.2016[phyto.2016$Microsite=="open","Plantago.biomass"], ylim=c(0,2))
# points(jitter(phyto.2016[phyto.2016$Microsite=="open","arid.gradient"] ,2), phyto.2016[phyto.2016$Microsite=="shrub","Plantago.biomass"], pch=19)
#
# plot(phyto.2016[phyto.2016$Microsite=="open","arid.gradient"] , rii.mean[rii.mean$Year==2016,"Plantago.biomass"], ylim=c(-0.4,0.4))
#
# ## Salvia season 1
# plot(jitter(phyto.2016[phyto.2016$Microsite=="open","arid.gradient"] ,1), phyto.2016[phyto.2016$Microsite=="open","Salvia.biomass"], ylim=c(0,2))
# points(jitter(phyto.2016[phyto.2016$Microsite=="open","arid.gradient"] ,2), phyto.2016[phyto.2016$Microsite=="shrub","Salvia.biomass"], pch=19)
#
# plot(phyto.2016[phyto.2016$Microsite=="open","arid.gradient"] , rii.mean[rii.mean$Year==2016,"Salvia.biomass"], ylim=c(-0.4,0.4))
#
#
#
# ## Phacelia season 2
# plot(jitter(gradient1.season2,1), phyto.2017[phyto.2017$Microsite=="open","Phacelia.biomass"], ylim=c(0,2))
# points(jitter(gradient1.season2,2), phyto.2017[phyto.2017$Microsite=="shrub","Phacelia.biomass"], pch=19)
#
# plot(gradient1.season2, rii.mean[rii.mean$Year==2017,"Phacelia.biomass"], ylim=c(-0.4,0.4))
#
#
# ## Plantago season 2
# plot(jitter(gradient1.season2,1), phyto.2017[phyto.2017$Microsite=="open","Plantago.biomass"], ylim=c(0,2))
# points(jitter(gradient1.season2,2), phyto.2017[phyto.2017$Microsite=="shrub","Plantago.biomass"], pch=19)
#
# plot(gradient1.season2, rii.mean[rii.mean$Year==2017,"Plantago.biomass"], ylim=c(-0.4,0.4))
#
# ## Salvia season 2
# plot(jitter(gradient1.season2,1), phyto.2017[phyto.2017$Microsite=="open","Salvia.biomass"], ylim=c(0,2))
# points(jitter(gradient1.season2,2), phyto.2017[phyto.2017$Microsite=="shrub","Salvia.biomass"], pch=19)
#
#
# plot(gradient1.season2, rii.mean[rii.mean$Year==2017,"Salvia.biomass"], ylim=c(-0.4,0.4))
## both seasons
## responses
plot(mean.phyto[mean.phyto$Microsite=="open","arid.gradient"], mean.phyto[mean.phyto$Microsite=="open","phyto.biomass"], ylim=c(0,3), ylab=c("all phytometer biomass (g)"))
points(jitter(mean.phyto[mean.phyto$Microsite=="shrub","arid.gradient"],1), mean.phyto[mean.phyto$Microsite=="shrub","phyto.biomass"], pch=19)
legend(-4.5,2.8, c("shrub","open"), pch=c(19,1))
text(-2.3, 2.8, "both years", cex=1.3)
#
# ## Phacelia
# plot(jitter(mean.phyto[mean.phyto$Microsite=="open","arid.gradient"],1), mean.phyto[mean.phyto$Microsite=="open","Phacelia.biomass"], ylim=c(0,2))
# points(jitter(mean.phyto[mean.phyto$Microsite=="shrub","arid.gradient"],2), mean.phyto[mean.phyto$Microsite=="shrub","Phacelia.biomass"], pch=19)
# m1 <- lm(ihs(Phacelia.biomass) ~ arid.gradient * Microsite, data=mean.phyto)
# summary(m1)
#
# ## Plantago
# plot(jitter(mean.phyto[mean.phyto$Microsite=="open","arid.gradient"],1), mean.phyto[mean.phyto$Microsite=="open","Plantago.biomass"], ylim=c(0,0.5))
# points(jitter(mean.phyto[mean.phyto$Microsite=="shrub","arid.gradient"],2), mean.phyto[mean.phyto$Microsite=="shrub","Plantago.biomass"], pch=19)
# m2 <- lm(ihs(Phacelia.biomass) ~ arid.gradient * Microsite, data=mean.phyto)
# summary(m2)
#
#
# ## Salvia season 1
# plot(jitter(mean.phyto[mean.phyto$Microsite=="open","arid.gradient"],1), mean.phyto[mean.phyto$Microsite=="open","Salvia.biomass"], ylim=c(0,2))
# points(jitter(mean.phyto[mean.phyto$Microsite=="shrub","arid.gradient"],2), mean.phyto[mean.phyto$Microsite=="shrub","Salvia.biomass"], pch=19)
# m3 <- lm(ihs(Salvia.biomass) ~ arid.gradient * Microsite, data=mean.phyto)
# summary(m3)
### Rii plots
rii.mean <- merge(rii.mean, site.climate, by=c("Year","Site"))
## biomass by RII
plot(mean.phyto[mean.phyto$Microsite=="open","Phacelia.biomass"], rii.mean[,"Phacelia.biomass"], ylim=c(-0.3,0.3), pch=19, ylab="RII biomass", xlab="average phytometer biomass per site")
points(mean.phyto[mean.phyto$Microsite=="open","Plantago.biomass"], rii.mean[,"Plantago.biomass"], pch=21, bg="blue")
points(mean.phyto[mean.phyto$Microsite=="open","Salvia.biomass"], rii.mean[,"Salvia.biomass"], pch=21, bg="red")
abline(h=0, lty=2, lwd=2)
legend(0.38,-0.15, c("Phacelia","Plantago","Salvia"), pch=21, pt.bg=c("black","blue","red"))
## rii variability
plot(rii.mean[,"arid.gradient"], rii.mean[,"Phacelia.biomass"], ylim=c(-0.5,0.5), pch=19, ylab="Rii phytometer biomass", xlab="aridity gradient")
points(rii.mean[,"arid.gradient"], rii.mean[,"Plantago.biomass"], pch=21, bg="blue")
points(rii.mean[,"arid.gradient"], rii.mean[,"Salvia.biomass"], pch=21, bg="red")
abline(h=0, lty=2, lwd=2)
legend(-4.5, 0.45, c("Phacelia","Plantago","Salvia"), pch=21, pt.bg=c("black","blue","red"))
## compare convex hull
## hull function
Plot_ConvexHull<-function(xcoord, ycoord, lcolor){
hpts <- chull(x = xcoord, y = ycoord)
hpts <- c(hpts, hpts[1])
lines(xcoord[hpts], ycoord[hpts], col = lcolor)
}
Plot_ConvexHull(xcoord = rii.mean[,"arid.gradient"], ycoord = rii.mean[,"Phacelia.biomass"], lcolor = "black")
Plot_ConvexHull(xcoord = rii.mean[,"arid.gradient"], ycoord = rii.mean[,"Plantago.biomass"], lcolor = "blue")
Plot_ConvexHull(xcoord = rii.mean[,"arid.gradient"], ycoord = rii.mean[,"Salvia.biomass"], lcolor = "red")
r.df <- rii.mean[,c("arid.gradient","Phacelia.biomass","Plantago.biomass","Salvia.biomass")]
## transform data into long format
r.df <- gather(r.df, species, biomass, 2:4)
## function to create a convex hull around data
hull.time <- function(df){
df[chull(df$arid.gradient,df$biomass),] # chull really is useful, even outside of contrived examples.
}
## break data into lists and recombined to apply polygon
splitData <- split(r.df, r.df$species)
appliedData <- lapply(splitData, hull.time)
combinedData <- do.call(rbind, appliedData)
## create polygon
poly1 <- ggplot(r.df, aes(x=arid.gradient, y=biomass))+
geom_polygon(data = combinedData, # This is also a nice example of how to plot
aes(x=arid.gradient, y=biomass, group = species, color=species), fill=NA, # two superimposed geoms
alpha = 1/2) + theme_bw()
poly1
## regress rii on gradient
m1 <- lm(Phacelia.biomass~arid.gradient, data=rii.mean)
summary(m1) ## not significant##
## Call:
## lm(formula = Phacelia.biomass ~ arid.gradient, data = rii.mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.022533 -0.013199 -0.005837 0.008594 0.058681
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.038659 0.020960 1.844 0.0899 .
## arid.gradient 0.008913 0.006828 1.305 0.2162
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02266 on 12 degrees of freedom
## Multiple R-squared: 0.1244, Adjusted R-squared: 0.05139
## F-statistic: 1.704 on 1 and 12 DF, p-value: 0.2162m2 <- lm(Plantago.biomass~arid.gradient, data=rii.mean)
summary(m2) ## not significant##
## Call:
## lm(formula = Plantago.biomass ~ arid.gradient, data = rii.mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.030560 -0.002438 0.002575 0.006742 0.019216
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.006458 0.014218 -0.454 0.658
## arid.gradient -0.001721 0.004632 -0.372 0.717
##
## Residual standard error: 0.01537 on 12 degrees of freedom
## Multiple R-squared: 0.01137, Adjusted R-squared: -0.07101
## F-statistic: 0.1381 on 1 and 12 DF, p-value: 0.7167m3 <- lm(Salvia.biomass~arid.gradient, data=rii.mean)
summary(m3) ## not significant##
## Call:
## lm(formula = Salvia.biomass ~ arid.gradient, data = rii.mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.051586 -0.015918 -0.001203 0.007707 0.066895
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.015346 0.029966 -0.512 0.618
## arid.gradient -0.004097 0.009762 -0.420 0.682
##
## Residual standard error: 0.0324 on 12 degrees of freedom
## Multiple R-squared: 0.01447, Adjusted R-squared: -0.06766
## F-statistic: 0.1761 on 1 and 12 DF, p-value: 0.6821#
# m1 <- lm(Phacelia.biomass~arid.gradient, data=subset(rii.mean,Year == 2016))
# m2 <- lm(Plantago.biomass~arid.gradient, data=subset(rii.mean,Year == 2016))
# m3 <- lm(Salvia.biomass~arid.gradient, data=subset(rii.mean,Year == 2016))
#
#
# m1 <- lm(Phacelia.biomass~arid.gradient, data=subset(rii.mean,Year == 2017))
# m2 <- lm(Plantago.biomass~arid.gradient, data=subset(rii.mean,Year == 2017))
# m3 <- lm(Salvia.biomass~arid.gradient, data=subset(rii.mean,Year == 2017))
#
#
# plot(rii.mean[,"min.temp"], rii.mean[,"Phacelia.biomass"], ylim=c(-0.4,0.4), pch=19)
# points(rii.mean[,"min.temp"], rii.mean[,"Plantago.biomass"], pch=21, bg="blue")
# points(rii.mean[,"min.temp"], rii.mean[,"Salvia.biomass"], pch=21, bg="red")
# abline(h=0, lty=2, lwd=2)
#
# rii.test <- subset(rii.mean, Gradient.x>3 & Year == 2017 | Year == 2016)
#
#
# plot(rii.test[,"arid.gradient"], rii.test[,"Phacelia.biomass"], ylim=c(-0.4,0.4), pch=19)
# points(rii.test[,"arid.gradient"], rii.test[,"Plantago.biomass"], pch=21, bg="blue")
# points(rii.test[,"arid.gradient"], rii.test[,"Salvia.biomass"], pch=21, bg="red")
# abline(h=0, lty=2, lwd=2)
#
#
# plot(rii.mean[,"arid.gradient"], rii.mean[,"Phacelia"], ylim=c(-0.4,0.4), pch=19)
# points(rii.mean[,"arid.gradient"], rii.mean[,"Plantago"], pch=21, bg="blue")
# points(rii.mean[,"arid.gradient"], rii.mean[,"Salvia"], pch=21, bg="red")
# abline(h=0, lty=2, lwd=2)
#
#
# m1 <- lm(Phacelia~ poly(arid.gradient,2), data=rii.mean)
# summary(m1)
# m2 <- lm(Plantago.biomass~arid.gradient, data=rii.mean)
# m3 <- lm(Salvia.biomass~arid.gradient, data=rii.mean)
Differences from Nutrient content
## join aridity with nutrients
nutrients.climate <- merge(nutrients,subset(site.climate, Year==2016), by=c("Site"))
## Nitrogen difference between shrub and sites
m.nit <- aov(log(N) ~ Site * microsite, data=nutrients)
summary(m.nit)## Df Sum Sq Mean Sq F value Pr(>F)
## Site 6 63.04 10.51 13.241 2.86e-09 ***
## microsite 1 40.16 40.16 50.614 2.24e-09 ***
## Site:microsite 6 4.96 0.83 1.042 0.408
## Residuals 56 44.43 0.79
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(m.nit, "microsite")## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = log(N) ~ Site * microsite, data = nutrients)
##
## $microsite
## diff lwr upr p adj
## shrub-open 1.514873 1.088317 1.941429 0## Potassium difference between shrub and sites
m.pot <- aov(log(K) ~ Site * microsite, data=nutrients)
summary(m.pot)## Df Sum Sq Mean Sq F value Pr(>F)
## Site 6 15.897 2.650 22.013 4.09e-13 ***
## microsite 1 4.445 4.445 36.934 1.14e-07 ***
## Site:microsite 6 1.605 0.268 2.223 0.0541 .
## Residuals 56 6.740 0.120
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(m.pot, "microsite")## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = log(K) ~ Site * microsite, data = nutrients)
##
## $microsite
## diff lwr upr p adj
## shrub-open 0.5040081 0.3378755 0.6701406 1e-07## Phosphorus difference between shrub and sites
m.pho <- aov(log(P) ~ Site * microsite, data=nutrients)
summary(m.pho)## Df Sum Sq Mean Sq F value Pr(>F)
## Site 6 23.163 3.861 14.33 8.10e-10 ***
## microsite 1 10.546 10.546 39.15 5.79e-08 ***
## Site:microsite 6 3.394 0.566 2.10 0.0676 .
## Residuals 56 15.085 0.269
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(m.pho, "microsite")## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = log(P) ~ Site * microsite, data = nutrients)
##
## $microsite
## diff lwr upr p adj
## shrub-open 0.7762734 0.5277317 1.024815 1e-07nutrient.mean <- nutrients.climate %>% group_by(Site, arid.gradient) %>% summarize(nitrogen=mean(N), phosphorus=mean(P), potassium=mean(K))
nutrient.mean <- data.frame(nutrient.mean)
ggplot(nutrient.mean) + geom_jitter(aes(x=arid.gradient, y=nitrogen), color="#E69F00", size=3) + stat_smooth(method="lm", formula= y~poly(x,2),aes(x=arid.gradient, y=nitrogen), color="#E69F00") + geom_jitter(aes(x=arid.gradient, y=potassium/20), color="#56B4E9", size=3) + stat_smooth(method="lm", formula= y~poly(x,2),aes(x=arid.gradient, y=potassium/20), color="#56B4E9") + geom_jitter(aes(x=arid.gradient, y=phosphorus), color="#009E73", size=3) +ylab("soil nutrient content")+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.line = element_line(colour = "black"),panel.background = element_blank(),axis.text=element_text(size=14),axis.title=element_text(size=16)) + xlab("aridity gradient")+
annotate("segment", x = -4.5, xend = -4.35, y = 25, yend = 25, colour = "#E69F00", size=2) + ## add custom legend
annotate("text", x = -4.3, y = 25, label = "Nitrogen", size=5, hjust=0)+
annotate("segment", x = -4.5, xend = -4.35, y = 23, yend = 23, colour = "#56B4E9", size=2) + ## add custom legend
annotate("text", x = -4.3, y = 23, label = "Potassium", size=5, hjust=0)+
annotate("segment", x = -4.5, xend = -4.35, y = 21, yend = 21, colour = "#009E73", size=2) + ## add custom legend
annotate("text", x = -4.3, y = 21, label = "Phosphorus", size=5, hjust=0)
m1 <- lm(nitrogen~poly(arid.gradient,2), data=nutrient.mean)
summary(m1)##
## Call:
## lm(formula = nitrogen ~ poly(arid.gradient, 2), data = nutrient.mean)
##
## Residuals:
## 1 2 3 4 5 6 7
## 1.1092 1.0570 -0.4565 -0.8511 -0.8789 -0.3757 0.3960
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.4326 0.3942 13.782 0.000161 ***
## poly(arid.gradient, 2)1 -12.6364 1.0429 -12.116 0.000266 ***
## poly(arid.gradient, 2)2 7.0832 1.0429 6.792 0.002454 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.043 on 4 degrees of freedom
## Multiple R-squared: 0.9797, Adjusted R-squared: 0.9695
## F-statistic: 96.47 on 2 and 4 DF, p-value: 0.0004126m2 <- lm(phosphorus~arid.gradient, data=nutrient.mean)
summary(m2)##
## Call:
## lm(formula = phosphorus ~ arid.gradient, data = nutrient.mean)
##
## Residuals:
## 1 2 3 4 5 6 7
## 4.3035 1.1485 -2.0485 1.1930 -0.2791 -5.5020 1.1845
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 14.191 4.527 3.135 0.0258 *
## arid.gradient 1.780 1.298 1.371 0.2287
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.383 on 5 degrees of freedom
## Multiple R-squared: 0.2732, Adjusted R-squared: 0.1278
## F-statistic: 1.879 on 1 and 5 DF, p-value: 0.2287m3 <- lm(potassium~poly(arid.gradient,2), data=nutrient.mean)
summary(m3)##
## Call:
## lm(formula = potassium ~ poly(arid.gradient, 2), data = nutrient.mean)
##
## Residuals:
## 1 2 3 4 5 6 7
## -3.551 -65.927 32.611 57.492 -25.220 52.524 -47.931
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 186.56 22.69 8.221 0.00119 **
## poly(arid.gradient, 2)1 189.58 60.04 3.157 0.03427 *
## poly(arid.gradient, 2)2 190.31 60.04 3.170 0.03387 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 60.04 on 4 degrees of freedom
## Multiple R-squared: 0.8334, Adjusted R-squared: 0.7502
## F-statistic: 10.01 on 2 and 4 DF, p-value: 0.02774annual plant community repsonse
spp.data <- read.csv("Data/ERG.communitydata.csv")
spp.data[is.na(spp.data)] <- 0
mean.spp <- spp.data %>% group_by(Year,Site, Microsite) %>% summarize(abd=mean(Abundance), richness=mean(Richness), biomass=mean(Biomass))
mean.spp <- data.frame(mean.spp)
## collect aridity gradient data
site.climate <- rbind(s1.mean,s2.mean)
site.climate[,"Year"] <- c(rep("2016",7),rep("2017",7))
## combine climate data with community data
mean.spp <- merge(mean.spp, site.climate, by.y=c("Year","Site"))
# ## community responses season1
# mean.spp2016 <- subset(mean.spp, Year==2016)
#
# ## richness
# plot(mean.spp2016[mean.spp2016$Microsite=="open","arid.gradient"], mean.spp2016[mean.spp2016$Microsite=="open","richness"], ylab="average site richness", xlab="aridity gradient")
# points(mean.spp2016[mean.spp2016$Microsite=="shrub","arid.gradient"],mean.spp2016[mean.spp2016$Microsite=="shrub","richness"], pch=19)
#
# m1 <- lm(richness~arid.gradient*Microsite, data=mean.spp2016)
# summary(m1)
#
# ## abundance
# plot(mean.spp2016[mean.spp2016$Microsite=="open","arid.gradient"], mean.spp2016[mean.spp2016$Microsite=="open","abd"], ylab="average site abundance", xlab="aridity gradient")
# points(mean.spp2016[mean.spp2016$Microsite=="shrub","arid.gradient"],mean.spp2016[mean.spp2016$Microsite=="shrub","abd"], pch=19)
#
# m2 <- lm(abd~arid.gradient*Microsite, data=mean.spp2016)
# summary(m2)
#
# ## biomass
# plot(mean.spp2016[mean.spp2016$Microsite=="open","arid.gradient"], mean.spp2016[mean.spp2016$Microsite=="open","biomass"], ylab="average site biomass", xlab="aridity gradient")
# points(mean.spp2016[mean.spp2016$Microsite=="shrub","arid.gradient"],mean.spp2016[mean.spp2016$Microsite=="shrub","biomass"], pch=19)
#
# m3 <- lm(biomass~arid.gradient*Microsite, data=mean.spp2016)
# summary(m3)
#
# ##season2
#
# ## community responses season2
#
# mean.spp2017 <- subset(mean.spp, Year==2017)
#
#
# ## richness
# plot(mean.spp2017[mean.spp2017$Microsite=="open","arid.gradient"], mean.spp2017[mean.spp2017$Microsite=="open","richness"])
# points(mean.spp2017[mean.spp2017$Microsite=="shrub","arid.gradient"],mean.spp2017[mean.spp2017$Microsite=="shrub","richness"], pch=19)
#
# m1 <- lm(richness~arid.gradient*Microsite, data=mean.spp2017)
# summary(m1)
#
# ## abundance
# plot(mean.spp2017[mean.spp2017$Microsite=="open","arid.gradient"], mean.spp2017[mean.spp2017$Microsite=="open","abd"])
# points(mean.spp2017[mean.spp2017$Microsite=="shrub","arid.gradient"],mean.spp2017[mean.spp2017$Microsite=="shrub","abd"], pch=19)
#
# m2 <- lm(abd~arid.gradient*Microsite, data=mean.spp2017)
# summary(m2)
#
# ## biomass
# plot(mean.spp2017[mean.spp2017$Microsite=="open","arid.gradient"], mean.spp2017[mean.spp2017$Microsite=="open","biomass"])
# points(mean.spp2017[mean.spp2017$Microsite=="shrub","arid.gradient"],mean.spp2017[mean.spp2017$Microsite=="shrub","biomass"], pch=19)
#
# m3 <- lm(biomass~arid.gradient*Microsite, data=mean.spp2017)
# summary(m3)
## both seasons
## richness
plot(mean.spp[mean.spp$Microsite=="open","arid.gradient"], mean.spp[mean.spp$Microsite=="open","richness"], ylab="average site richness", xlab="aridity gradient")
points(mean.spp[mean.spp$Microsite=="shrub","arid.gradient"],mean.spp[mean.spp$Microsite=="shrub","richness"], pch=19) 
m1 <- lm(richness~arid.gradient*Microsite, data=mean.spp)
summary(m1)##
## Call:
## lm(formula = richness ~ arid.gradient * Microsite, data = mean.spp)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.56169 -0.66225 -0.07639 0.66154 1.76433
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.9206 0.8381 5.871 4.68e-06 ***
## arid.gradient 0.8699 0.2730 3.186 0.00397 **
## Micrositeshrub -1.8968 1.1852 -1.600 0.12260
## arid.gradient:Micrositeshrub -0.5531 0.3861 -1.432 0.16490
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9061 on 24 degrees of freedom
## Multiple R-squared: 0.3356, Adjusted R-squared: 0.2526
## F-statistic: 4.042 on 3 and 24 DF, p-value: 0.01849m1 <- lm(richness~poly(arid.gradient,2), data=mean.spp)
summary(m1)##
## Call:
## lm(formula = richness ~ poly(arid.gradient, 2), data = mean.spp)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.09400 -0.58831 -0.06033 0.47242 1.63726
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.2286 0.1418 15.719 1.81e-14 ***
## poly(arid.gradient, 2)1 2.7847 0.7502 3.712 0.001034 **
## poly(arid.gradient, 2)2 -2.7989 0.7502 -3.731 0.000986 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7502 on 25 degrees of freedom
## Multiple R-squared: 0.5256, Adjusted R-squared: 0.4876
## F-statistic: 13.85 on 2 and 25 DF, p-value: 8.954e-05ggplot(mean.spp) + geom_jitter(aes(x=arid.gradient, y=richness, fill=Microsite, color=Microsite), size=3) +
scale_color_manual(values=c("#E69F00","#56B4E9")) + stat_smooth(method="lm", formula= y~poly(x,2),aes(x=arid.gradient, y=richness), color="black")+ ylab("Species richness") + xlab("Aridity Gradient")+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.line = element_line(colour = "black"),panel.background = element_blank(),axis.text=element_text(size=14),axis.title=element_text(size=16)) 
## abundance
plot(mean.spp[mean.spp$Microsite=="open","arid.gradient"], mean.spp[mean.spp$Microsite=="open","abd"], ylab="average site abundance", xlab="aridity gradient")
points(mean.spp[mean.spp$Microsite=="shrub","arid.gradient"],mean.spp[mean.spp$Microsite=="shrub","abd"], pch=19) 
m2 <- lm(abd~arid.gradient*Microsite, data=mean.spp)
summary(m2)##
## Call:
## lm(formula = abd ~ arid.gradient * Microsite, data = mean.spp)
##
## Residuals:
## Min 1Q Median 3Q Max
## -64.997 -26.611 -5.081 25.895 108.541
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 162.65 38.28 4.249 0.00028 ***
## arid.gradient 41.28 12.47 3.310 0.00294 **
## Micrositeshrub 39.22 54.14 0.724 0.47582
## arid.gradient:Micrositeshrub 10.04 17.64 0.569 0.57457
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 41.39 on 24 degrees of freedom
## Multiple R-squared: 0.5409, Adjusted R-squared: 0.4835
## F-statistic: 9.426 on 3 and 24 DF, p-value: 0.000268m2 <- lm(ihs(abd)~arid.gradient, data=mean.spp)
summary(m2)##
## Call:
## lm(formula = ihs(abd) ~ arid.gradient, data = mean.spp)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.18927 -0.43160 -0.06177 0.41813 1.50236
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.6323 0.4214 20.48 < 2e-16 ***
## arid.gradient 1.7161 0.1373 12.50 1.69e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6444 on 26 degrees of freedom
## Multiple R-squared: 0.8573, Adjusted R-squared: 0.8519
## F-statistic: 156.3 on 1 and 26 DF, p-value: 1.687e-12ggplot(mean.spp) + geom_jitter(aes(x=arid.gradient, y=ihs(abd), fill=Microsite, color=Microsite), size=3)+
scale_color_manual(values=c("#E69F00","#56B4E9")) + stat_smooth(method="lm", formula= y~x,aes(x=arid.gradient, y=ihs(abd)), color="black")+ ylab("Annual abundance") + xlab("aridity gradient")+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.line = element_line(colour = "black"),panel.background = element_blank(),axis.text=element_text(size=14),axis.title=element_text(size=16)) 
## biomass
plot(mean.spp[mean.spp$Microsite=="open","arid.gradient"], ihs(mean.spp[mean.spp$Microsite=="open","biomass"]), ylab="average site biomass", xlab="aridity gradient")
points(mean.spp[mean.spp$Microsite=="shrub","arid.gradient"],ihs(mean.spp[mean.spp$Microsite=="shrub","biomass"]), pch=19)
m3 <- lm(ihs(biomass)~arid.gradient*Microsite, data=mean.spp)
summary(m3)##
## Call:
## lm(formula = ihs(biomass) ~ arid.gradient * Microsite, data = mean.spp)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.6596 -0.5085 -0.0684 0.3314 1.2077
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.6251 0.5577 4.707 8.75e-05 ***
## arid.gradient 0.5873 0.1817 3.233 0.00355 **
## Micrositeshrub 1.6603 0.7888 2.105 0.04596 *
## arid.gradient:Micrositeshrub 0.3698 0.2569 1.439 0.16303
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.603 on 24 degrees of freedom
## Multiple R-squared: 0.6498, Adjusted R-squared: 0.606
## F-statistic: 14.84 on 3 and 24 DF, p-value: 1.128e-05ggplot(mean.spp) + geom_jitter(aes(x=arid.gradient, y=ihs(biomass), fill=Microsite, color=Microsite), size=3)+
scale_color_manual(values=c("#E69F00","#56B4E9"))+ stat_smooth(method="lm", formula= y~x,aes(x=arid.gradient, y=ihs(biomass)), color="#56B4E9", data=subset(mean.spp, Microsite=="shrub")) + stat_smooth(method="lm", formula= y~x,aes(x=arid.gradient, y=ihs(biomass)), data=subset(mean.spp, Microsite=="open"), color="#E69F00")+ ylab("Annual biomass") + xlab("aridity gradient")+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.line = element_line(colour = "black"),panel.background = element_blank(),axis.text=element_text(size=14),axis.title=element_text(size=16)) 
RDA community and environmental data
community <- read.csv("Data/ERG.communitydata.csv")
community[is.na(community)] <- 0
##2016
## add enviro data to dataframe
comm2016 <- subset(community, Year==2016)
site.vars2016[,"Site"] <- as.factor(row.names(site.vars2016))
comm2016 <- merge(comm2016, site.vars2016, by="Site")
## transform spp data
community.trans <- decostand(comm2016[,13:53], "hellinger")
rda1 <- rda(community.trans, comm2016[,54:57])
(R2adj <- RsquareAdj(rda1)$adj.r.squared) ## 50.4% of variation explained## [1] 0.3871872## build byplot manually
par(mar=c(4.5,4.5,0.5,0.5))
plot(rda1, type="n", xlim=c(-1,1), ylim=c(-1.5,1.5))
## calculate priority
spp.priority <- colSums(comm2016[,13:53])
## plot RDA1
colvec <- rep(c("blue","dodgerblue3","cyan","yellow2","orange","tomato","red2"),each=60)
points(rda1, display = "sites", col = "black", pch = c(21), bg = colvec)
orditorp(rda1, display = "species", cex = 0.7, col = "darkred", priority=spp.priority, air=0.5)
text(rda1, display = "bp", col = "blue", cex = 0.8)
legend("bottomright", pch=c(21), legend=unique(comm2016$Site), pt.bg=unique(colvec), cex=1)
## ordikplot to adjust species locations
## 2017
## add enviro data to dataframe
comm2017 <- subset(community, Year==2017)
site.vars[,"Site"] <- as.factor(row.names(site.vars))
comm2017 <- merge(comm2017, site.vars, by="Site")
#
# ## transform spp data
# community.trans <- decostand(comm2017[,13:53], "hellinger")
#
# rda2 <- rda(community.trans, comm2017[,54:58])
# (R2adj <- RsquareAdj(rda2)$adj.r.squared) ## 50.4% of variation explained
#
#
# ## build byplot manually
# par(mar=c(4.5,4.5,0.5,0.5))
# plot(rda2, type="n", xlim=c(-1,1), ylim=c(-1.5,1.5))
#
# ## calculate priority
# spp.priority <- colSums(comm2017[,13:53])
#
# ## plot RDA2
# colvec <- rep(c("blue","dodgerblue3","cyan","yellow2","orange","tomato","red2"),each=60)
# points(rda2, display = "sites", col = "black", pch = c(21), bg = colvec)
# orditorp(rda2, display = "species", cex = 0.7, col = "darkred", priority=spp.priority, air=0.5)
# text(rda2, display = "bp", col = "blue", cex = 0.8)
# legend("bottomright", pch=c(21), legend=unique(comm2016$Site), pt.bg=unique(colvec), cex=1)
# ## ordikplot to adjust species locationsPhytometer analysis
census <- read.csv("Data/ERG.phytometer.census.csv")
census[is.na(census)] <- 0
census[,"phyto.abd"] <- rowSums(census[,c("Phacelia","Plantago","Salvia")])
## calculate the number of seeds per gram
seed.mass <- read.csv("Data/seed.mass.csv")
seed.mass.avg <- seed.mass %>% group_by(species) %>% summarize(avg.seed.gram=mean(seed.number),se.seed.gram=se(seed.number))
seed.mass.avg <- data.frame(seed.mass.avg)
seed.mass.avg## species avg.seed.gram se.seed.gram
## 1 Amsinkia 235.2 7.611833
## 2 Caulanthus 3019.0 8.916277
## 3 Lipidium 752.4 4.261455
## 4 Monolopia 737.8 7.831986
## 5 Phacelia 772.0 5.300943
## 6 Plantago 558.2 6.865858
## 7 Salvia 980.0 9.612492## get proportion of seed germinated per 0.3 grams of seed
census[,"Phacelia.prop"] <- census[,"Phacelia"]/(seed.mass.avg$avg.seed.gram[which(seed.mass.avg$species=="Phacelia")]*0.3)
census[,"Plantago.prop"] <- census[,"Plantago"]/(seed.mass.avg$avg.seed.gram[which(seed.mass.avg$species=="Plantago")]*0.3)
census[,"Salvia.prop"] <- census[,"Salvia"]/(seed.mass.avg$avg.seed.gram[which(seed.mass.avg$species=="Salvia")]*0.3)
## beginning
census.int <- subset(census, Census=="emergence")
## all species
m1 <- glm.nb(Phacelia~ Microsite * Nutrient * Site + Year, data=census.int)
anova(m1, test="Chisq") ## Site * Micro and Year significant## Analysis of Deviance Table
##
## Model: Negative Binomial(0.8108), link: log
##
## Response: Phacelia
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 839 1079.71
## Microsite 1 10.304 838 1069.41 0.001328 **
## Nutrient 1 1.234 837 1068.17 0.266538
## Site 6 159.651 831 908.52 < 2.2e-16 ***
## Year 1 31.424 830 877.10 2.073e-08 ***
## Microsite:Nutrient 1 0.046 829 877.05 0.829874
## Microsite:Site 6 21.032 823 856.02 0.001811 **
## Nutrient:Site 6 5.542 817 850.48 0.476389
## Microsite:Nutrient:Site 6 8.030 811 842.45 0.235879
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Phacelia
m1 <- glm.nb(Phacelia~ Microsite * Nutrient * Site + Year, data=census.int)
anova(m1, test="Chisq") ## Site * Micro and Year significant## Analysis of Deviance Table
##
## Model: Negative Binomial(0.8108), link: log
##
## Response: Phacelia
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 839 1079.71
## Microsite 1 10.304 838 1069.41 0.001328 **
## Nutrient 1 1.234 837 1068.17 0.266538
## Site 6 159.651 831 908.52 < 2.2e-16 ***
## Year 1 31.424 830 877.10 2.073e-08 ***
## Microsite:Nutrient 1 0.046 829 877.05 0.829874
## Microsite:Site 6 21.032 823 856.02 0.001811 **
## Nutrient:Site 6 5.542 817 850.48 0.476389
## Microsite:Nutrient:Site 6 8.030 811 842.45 0.235879
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Plantago
m2 <- glm.nb(Plantago~ Microsite * Nutrient * Site + Year, data=census.int)
anova(m2, test="Chisq") ## Site * Micro and Year significant## Analysis of Deviance Table
##
## Model: Negative Binomial(0.5335), link: log
##
## Response: Plantago
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 839 984.30
## Microsite 1 6.256 838 978.04 0.01238 *
## Nutrient 1 1.060 837 976.98 0.30332
## Site 6 162.687 831 814.30 < 2.2e-16 ***
## Year 1 4.138 830 810.16 0.04194 *
## Microsite:Nutrient 1 2.388 829 807.77 0.12224
## Microsite:Site 6 43.000 823 764.77 1.167e-07 ***
## Nutrient:Site 6 6.391 817 758.38 0.38088
## Microsite:Nutrient:Site 6 4.615 811 753.77 0.59403
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Salvia
m3 <- glm.nb(Salvia~ Microsite * Nutrient * Site + Year, data=census.int)
anova(m3, test="Chisq") ## Site * Micro and Year significant + (micro x nutrient)## Analysis of Deviance Table
##
## Model: Negative Binomial(0.8273), link: log
##
## Response: Salvia
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 839 1210.25
## Microsite 1 5.924 838 1204.33 0.0149386 *
## Nutrient 1 1.513 837 1202.82 0.2186678
## Site 6 211.625 831 991.19 < 2.2e-16 ***
## Year 1 17.463 830 973.73 2.929e-05 ***
## Microsite:Nutrient 1 7.824 829 965.90 0.0051548 **
## Microsite:Site 6 26.421 823 939.48 0.0001858 ***
## Nutrient:Site 6 4.587 817 934.90 0.5977624
## Microsite:Nutrient:Site 6 1.566 811 933.33 0.9549811
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1census.int.plot <- gather(census.int, species, abundance, Phacelia:Salvia)
##calculate confidence interval
census.plot<- census.int.plot %>% group_by(Microsite, species, Site, Year) %>% summarize(avg=mean(abundance),ci=se(abundance)*1.96)
ggplot(census.plot, aes(x=Microsite, y=avg, fill=species))+
geom_bar(position=position_dodge(), stat="identity")+
geom_errorbar(aes(ymin=avg-ci, ymax=avg+ci),
width=.2, # Width of the error bars
position=position_dodge(.9))+ scale_fill_brewer() +ylab("Abundance")+ theme_Publication() + facet_grid(Year~Site) 
## end season
census.end <- subset(census, Census=="end")
census.end[,"Year"] <- as.factor(census.end$Year)
## run full model for phyto biomass
m1 <- aov(log(phyto.biomass)~ Site * Microsite * Nutrient + Year, data=subset(census.end, phyto.biomass>0))
summary(m1)## Df Sum Sq Mean Sq F value Pr(>F)
## Site 6 330.8 55.13 32.164 < 2e-16 ***
## Microsite 1 10.9 10.88 6.347 0.012127 *
## Nutrient 1 30.3 30.25 17.651 3.24e-05 ***
## Year 1 161.2 161.21 94.051 < 2e-16 ***
## Site:Microsite 6 23.9 3.99 2.327 0.031940 *
## Site:Nutrient 6 41.9 6.98 4.073 0.000551 ***
## Microsite:Nutrient 1 1.7 1.73 1.011 0.315196
## Site:Microsite:Nutrient 6 7.9 1.32 0.768 0.595068
## Residuals 424 726.7 1.71
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(m1, "Microsite")## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = log(phyto.biomass) ~ Site * Microsite * Nutrient + Year, data = subset(census.end, phyto.biomass > 0))
##
## $Microsite
## diff lwr upr p adj
## shrub-open 0.3096905 0.06750868 0.5518722 0.0123243## all species end of season
m2 <- glm.nb(phyto.abd~ Microsite * Nutrient * Site + Year, data=census.end)
anova(m2, test="Chisq") ## Site and microsite*nutrient significant## Analysis of Deviance Table
##
## Model: Negative Binomial(0.455), link: log
##
## Response: phyto.abd
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 839 1009.51
## Microsite 1 1.059 838 1008.45 0.30341
## Nutrient 1 2.527 837 1005.92 0.11194
## Site 6 135.420 831 870.50 < 2e-16 ***
## Year 1 3.412 830 867.09 0.06474 .
## Microsite:Nutrient 1 4.586 829 862.50 0.03224 *
## Microsite:Site 6 5.340 823 857.16 0.50104
## Nutrient:Site 6 5.169 817 852.00 0.52238
## Microsite:Nutrient:Site 6 5.683 811 846.31 0.45956
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## run full model for phacelia biomass
m3 <- aov(log(Phacelia.biomass)~ Site * Microsite * Nutrient + Year, data=subset(census.end, Phacelia.biomass>0))
summary(m3) ## site and microsite## Df Sum Sq Mean Sq F value Pr(>F)
## Site 6 89.7 14.95 8.274 4.95e-08 ***
## Microsite 1 21.2 21.20 11.738 0.00074 ***
## Nutrient 1 10.2 10.19 5.642 0.01845 *
## Year 1 95.5 95.49 52.857 7.42e-12 ***
## Site:Microsite 6 23.6 3.93 2.173 0.04692 *
## Site:Nutrient 6 8.0 1.34 0.742 0.61612
## Microsite:Nutrient 1 0.0 0.00 0.001 0.97552
## Site:Microsite:Nutrient 5 4.1 0.82 0.452 0.81122
## Residuals 205 370.3 1.81
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(m3, "Microsite")## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = log(Phacelia.biomass) ~ Site * Microsite * Nutrient + Year, data = subset(census.end, Phacelia.biomass > 0))
##
## $Microsite
## diff lwr upr p adj
## shrub-open 0.6312547 0.2667053 0.9958041 0.0007714## run full model for plantago biomass
m4 <- aov(log(Plantago.biomass)~ Site * Microsite * Nutrient + Year, data=subset(census.end, Plantago.biomass>0))
summary(m4) ## site and year + sitexmicrosite## Df Sum Sq Mean Sq F value Pr(>F)
## Site 6 116.35 19.39 19.843 2.32e-16 ***
## Microsite 1 1.96 1.96 2.001 0.160
## Nutrient 1 32.03 32.03 32.778 6.80e-08 ***
## Year 1 138.38 138.38 141.597 < 2e-16 ***
## Site:Microsite 6 4.87 0.81 0.830 0.549
## Site:Nutrient 4 1.99 0.50 0.508 0.730
## Microsite:Nutrient 1 3.17 3.17 3.244 0.074 .
## Site:Microsite:Nutrient 3 0.65 0.22 0.220 0.882
## Residuals 130 127.05 0.98
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(m4, "Microsite")## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = log(Plantago.biomass) ~ Site * Microsite * Nutrient + Year, data = subset(census.end, Plantago.biomass > 0))
##
## $Microsite
## diff lwr upr p adj
## shrub-open -0.2179629 -0.5348829 0.0989571 0.1759823## run full model for plantago biomass
m5 <- aov(log(Salvia.biomass)~ Site * Microsite * Nutrient + Year, data=subset(census.end, Salvia.biomass>0))
summary(m5) ## site and year + sitexmicrosite## Df Sum Sq Mean Sq F value Pr(>F)
## Site 6 170.1 28.35 19.982 < 2e-16 ***
## Microsite 1 0.0 0.01 0.008 0.929221
## Nutrient 1 21.4 21.43 15.103 0.000124 ***
## Year 1 44.1 44.05 31.045 5.39e-08 ***
## Site:Microsite 6 6.8 1.14 0.802 0.568988
## Site:Nutrient 6 46.2 7.70 5.424 2.35e-05 ***
## Microsite:Nutrient 1 0.5 0.55 0.387 0.534459
## Site:Microsite:Nutrient 6 12.9 2.16 1.520 0.171069
## Residuals 317 449.8 1.42
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(m5, "Microsite")## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = log(Salvia.biomass) ~ Site * Microsite * Nutrient + Year, data = subset(census.end, Salvia.biomass > 0))
##
## $Microsite
## diff lwr upr p adj
## shrub-open 0.01133515 -0.2406604 0.2633307 0.9295351## run full model for phacelia abundance
m6 <- glm.nb(Phacelia.biomass~ Site * Microsite * Nutrient + Year, data=subset(census.end, Phacelia>0))
summary(m3) ## site and microsite## Df Sum Sq Mean Sq F value Pr(>F)
## Site 6 89.7 14.95 8.274 4.95e-08 ***
## Microsite 1 21.2 21.20 11.738 0.00074 ***
## Nutrient 1 10.2 10.19 5.642 0.01845 *
## Year 1 95.5 95.49 52.857 7.42e-12 ***
## Site:Microsite 6 23.6 3.93 2.173 0.04692 *
## Site:Nutrient 6 8.0 1.34 0.742 0.61612
## Microsite:Nutrient 1 0.0 0.00 0.001 0.97552
## Site:Microsite:Nutrient 5 4.1 0.82 0.452 0.81122
## Residuals 205 370.3 1.81
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(m3, "Microsite")## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = log(Phacelia.biomass) ~ Site * Microsite * Nutrient + Year, data = subset(census.end, Phacelia.biomass > 0))
##
## $Microsite
## diff lwr upr p adj
## shrub-open 0.6312547 0.2667053 0.9958041 0.0007714## run full model for plantago biomass
m7 <- aov(log(Plantago)~ Site * Microsite * Nutrient + Year, data=subset(census.end, Plantago>0))
summary(m4) ## site and year + sitexmicrosite## Df Sum Sq Mean Sq F value Pr(>F)
## Site 6 116.35 19.39 19.843 2.32e-16 ***
## Microsite 1 1.96 1.96 2.001 0.160
## Nutrient 1 32.03 32.03 32.778 6.80e-08 ***
## Year 1 138.38 138.38 141.597 < 2e-16 ***
## Site:Microsite 6 4.87 0.81 0.830 0.549
## Site:Nutrient 4 1.99 0.50 0.508 0.730
## Microsite:Nutrient 1 3.17 3.17 3.244 0.074 .
## Site:Microsite:Nutrient 3 0.65 0.22 0.220 0.882
## Residuals 130 127.05 0.98
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(m4, "Microsite")## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = log(Plantago.biomass) ~ Site * Microsite * Nutrient + Year, data = subset(census.end, Plantago.biomass > 0))
##
## $Microsite
## diff lwr upr p adj
## shrub-open -0.2179629 -0.5348829 0.0989571 0.1759823## run full model for plantago biomass
m8 <- aov(log(Salvia)~ Site * Microsite * Nutrient + Year, data=subset(census.end, Salvia>0))
summary(m5) ## site and year + sitexmicrosite## Df Sum Sq Mean Sq F value Pr(>F)
## Site 6 170.1 28.35 19.982 < 2e-16 ***
## Microsite 1 0.0 0.01 0.008 0.929221
## Nutrient 1 21.4 21.43 15.103 0.000124 ***
## Year 1 44.1 44.05 31.045 5.39e-08 ***
## Site:Microsite 6 6.8 1.14 0.802 0.568988
## Site:Nutrient 6 46.2 7.70 5.424 2.35e-05 ***
## Microsite:Nutrient 1 0.5 0.55 0.387 0.534459
## Site:Microsite:Nutrient 6 12.9 2.16 1.520 0.171069
## Residuals 317 449.8 1.42
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(m5, "Microsite")## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = log(Salvia.biomass) ~ Site * Microsite * Nutrient + Year, data = subset(census.end, Salvia.biomass > 0))
##
## $Microsite
## diff lwr upr p adj
## shrub-open 0.01133515 -0.2406604 0.2633307 0.9295351census.end.plot <- gather(census.end, species, biomass, Phacelia.biomass:Salvia.biomass)
##calculate confidence interval
census.plot<- census.end.plot %>% group_by(Microsite, species, Site, Year) %>% summarize(avg=mean(biomass),ci=se(biomass)*1.96)
ggplot(census.plot, aes(x=Microsite, y=avg, fill=species))+
geom_bar(position=position_dodge(), stat="identity")+
geom_errorbar(aes(ymin=avg-ci, ymax=avg+ci),
width=.2, # Width of the error bars
position=position_dodge(.9))+ scale_fill_brewer() +ylab("Abundance")+ theme_Publication() + facet_grid(Year~Site) 
### RDA
#
# ##collect environmental variables for site
# nutrients <- read.csv("Data/ERG.soilnutrients.csv")
# nutrients.mean <- nutrients %>% group_by(gradient,site, microsite) %>% summarise_each(funs(mean))
# nutrients.vars <- data.frame(nutrients.mean)
#
# ## minimum explanation of variables
# ambient2016[is.na(ambient2016)] <- 0
# community <- ambient2016 %>% group_by(Gradient,Site, Microsite) %>% summarise_each(funs(sum))
# community <- data.frame(community)
#
# end.census <- subset(census2016, census=="end")
# end.census <- end.census %>% group_by(Gradient,Site, Microsite) %>% summarise_each(funs(mean))
# end.census<- data.frame(end.census)
#
# ## daily means
# day.mean <- aggregate(HOBO.data, by=list(day=HOBO.data$Day, micro=HOBO.data$Microsite, site=HOBO.data$Site), mean)
#
# ## summarize daily means across sites
# means <- day.mean %>% group_by(gradient, micro) %>% summarize(temp=mean(Temp),rh=mean(RH),temp.se=se(Temp),rh.se=se(RH))
# means <- data.frame(means)
#
#
# envs <- data.frame(swc=end.census[,"swc"],nutrients.vars[,c("N","P","K")], means[,c("temp","rh")])
#
# ## hellinger transformation
# community.trans <- decostand(community[,12:42], "hellinger")
#
# ## rda with environmental variables
# rda1 <- rda(community.trans, envs)
# anova(rda1)
# (R2adj <- RsquareAdj(rda1)$adj.r.squared) ## 25.7% of variation explained
#
#
# ## build byplot manually
# par(mar=c(4.5,4.5,0.5,0.5))
# plot(rda1, type="n", xlim=c(-1,1))
#
# with(community, levels(Site))
#
# spp.priority <- colSums(community[,12:42])
#
# colvec <- c("blue","dodgerblue3","cyan","yellow2","orange","tomato","red2")
# with(community, points(rda1, display = "sites", col = "black", pch = c(21,22), bg = colvec[Site]))
# orditorp(rda1, display = "species", cex = 0.7, col = "darkred", priority=spp.priority, air=1,)
# text(rda1, display = "bp", col = "blue", cex = 0.8)
# legend("bottomright", pch=c(21), legend=community$Site[community$Microsite=="shrub"], pt.bg=colvec, cex=1)
#
# ## check variable explanation
# vif.cca(rda1)
#
# v.clim <- cbind(envs[,c("temp","rh")])
# v.nut <- cbind(envs[,c("N","P","K")])
# v.swc <- envs[,"swc"]
#
# x <- varpart(community.trans,v.clim,v.nut,v.swc)
#
# showvarparts(3)Difference in effect size
library(bootES)## Loading required package: boot##
## Attaching package: 'boot'## The following object is masked from 'package:lattice':
##
## melanomaeffect.cal <- function(x){ bootES(x, R=999, data.col="Biomass", group.col="Microsite", contrast=c(shrub=1,open=-1), effect.type=c("cohens.d"))}
effect.site <- by(subset(comm2016, Site != "Barstow"), comm2016$Site[comm2016$Site != "Barstow"], FUN=effect.cal)
site.summary <- rbind(summary(effect.site$Panoche),summary(effect.site$Cuyama),summary(effect.site$TejonRanch),data.frame(stat=0,ci.low=0,ci.high=0,bias=0,std.error=0),summary(effect.site$MojavePreserve),summary(effect.site$SheepholeValley),summary(effect.site$Tecopa))
site.summary[5,] <- 0
site.summary <- sapply(site.summary, as.numeric)
par(mar=c(4.5,4.5,.5,.5))
plot(c(1:7),site.summary[,"stat"], xlim=c(0.5,7.5), ylim=c(-1,3), pch=19, cex=1.4, ylab="Hedge's G", xlab="", xaxt="n")
arrows(c(1:7),as.numeric(site.summary[,"ci.high"]),c(1:7),site.summary[,"ci.low"], angle=90, code=3, length=0, lwd=2)
abline(h=0, lty=2, lwd=2)
axis(1, c(1:7), labels=site.names, cex.axis=1)
## phytometer
effect.cal <- function(x){ bootES(x, R=999, data.col="phyto.biomass", group.col="Microsite", contrast=c(shrub=1,open=-1), effect.type=c("cohens.d"))}
census2016 <- subset(census, Census=="end" & Year ==2016)
effect.site <- by(subset(census2016, Site != "Barstow"), census2016$Site[census2016$Site != "Barstow"], FUN=effect.cal)
site.summary <- rbind(summary(effect.site$Panoche),summary(effect.site$Cuyama),summary(effect.site$TejonRanch),data.frame(stat=0,ci.low=0,ci.high=0,bias=0,std.error=0),summary(effect.site$MojavePreserve),summary(effect.site$SheepholeValley),summary(effect.site$Tecopa))
site.summary <- sapply(site.summary, as.numeric)
par(mar=c(4.5,4.5,.5,.5))
plot(c(1:7),site.summary[,"stat"], xlim=c(0.5,7.5), ylim=c(-1,3), pch=19, cex=1.4, ylab="Hedge's G", xlab="", xaxt="n")
arrows(c(1:7),site.summary[,"ci.high"],c(1:7),site.summary[,"ci.low"], angle=90, code=3, length=0, lwd=2)
abline(h=0, lty=2, lwd=2)
axis(1, c(1:7), labels=site.names, cex.axis=1)
Rii between years
rii.dat <- rii(community, j=1:8, var=c("Abundance","Richness","Biomass"))
rii.mean <- rii.dat %>% group_by(Year, Gradient, Site) %>% summarize(avg=mean(Biomass),se=se(Biomass))
rii.mean <- data.frame(rii.mean)
plot(rii.mean[rii.mean$Year==2016,"Gradient"]-.1,rii.mean[rii.mean$Year==2016,"avg"], ylim=c(-1,1), pch=19, cex=1.5, xlim=c(0.5,7.5), ylab="Rii Biomass", xlab="Site", xaxt="n", cex.lab=1.5)
axis(1, 1:7, lab=unique(rii.mean$Site))
error.bar(rii.mean[rii.mean$Year==2016,"Gradient"]-.1,rii.mean[rii.mean$Year==2016,"avg"],rii.mean[rii.mean$Year==2016,"se"])
error.bar(rii.mean[rii.mean$Year==2017,"Gradient"]+.1,rii.mean[rii.mean$Year==2017,"avg"],rii.mean[rii.mean$Year==2017,"se"])
points(rii.mean[rii.mean$Year==2017,"Gradient"]+.1,rii.mean[rii.mean$Year==2017,"avg"], pch=21, bg="Grey50", cex=1.5)
abline(h=0, lwd=2, lty=2)
legend(6.3,-0.55, c("2016","2017"), pch=22, pt.bg=c("Black","Grey50"), cex=1.6)
## add year column to precipitation data
precip[,"Year"] <- ifelse(precip$season=="season.1","2016","2017")
rii.precip <- merge(precip,rii.mean, by=c("Year","Gradient"))Native non-native comparisons
## load species list
spp.list <- read.csv("Data/ERG.specieslist.csv")
## convert data to long format
status <- gather(community, species, abundance, 13:53)
names(spp.list)[1] <- "species" ## rename species column for merge
## combine native-non-native status with data set
status <- merge(status, spp.list, by="species")
## summarize per plot native and non-natives, convert back to wide format
mean.status <- status %>% group_by(Year, Site, Microsite, Rep, status) %>% summarize(abundance=sum(abundance))
mean.status <- spread(mean.status, status, abundance)
## calculate site means for native and non-native
mean.status <- mean.status %>% group_by(Year, Site, Microsite) %>% summarize(native=mean(native), non.native=mean(non.native))
## merge with original community data
mean.spp <- merge(mean.spp, mean.status, by=c("Year","Site","Microsite"))
## native
m1 <- lm(ihs(native) ~ poly(arid.gradient,2), data=subset(mean.spp, Microsite=="shrub"))
summary(m1)##
## Call:
## lm(formula = ihs(native) ~ poly(arid.gradient, 2), data = subset(mean.spp,
## Microsite == "shrub"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.03332 -0.65782 -0.04555 0.60465 1.25478
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.6533 0.2185 7.565 1.11e-05 ***
## poly(arid.gradient, 2)1 0.3447 0.8177 0.422 0.68145
## poly(arid.gradient, 2)2 -2.9468 0.8177 -3.604 0.00414 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8177 on 11 degrees of freedom
## Multiple R-squared: 0.5448, Adjusted R-squared: 0.4621
## F-statistic: 6.583 on 2 and 11 DF, p-value: 0.01318m1 <- lm(ihs(native) ~ arid.gradient, data=subset(mean.spp, Microsite=="open"))
summary(m1)##
## Call:
## lm(formula = ihs(native) ~ arid.gradient, data = subset(mean.spp,
## Microsite == "open"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.89497 -0.73527 0.02181 0.50785 1.81716
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.0097 1.0111 3.966 0.00187 **
## arid.gradient 0.6575 0.3294 1.996 0.06913 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.093 on 12 degrees of freedom
## Multiple R-squared: 0.2493, Adjusted R-squared: 0.1867
## F-statistic: 3.984 on 1 and 12 DF, p-value: 0.06913m1 <- lm(ihs(native) ~ poly(arid.gradient,2)*Microsite, data=mean.spp)
summary(m1)##
## Call:
## lm(formula = ihs(native) ~ poly(arid.gradient, 2) * Microsite,
## data = mean.spp)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.6483 -0.6647 -0.1444 0.5383 2.0863
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 2.0776 0.2616 7.943
## poly(arid.gradient, 2)1 3.0858 1.3841 2.229
## poly(arid.gradient, 2)2 -1.1145 1.3841 -0.805
## Micrositeshrub -0.4243 0.3699 -1.147
## poly(arid.gradient, 2)1:Micrositeshrub -2.5983 1.9574 -1.327
## poly(arid.gradient, 2)2:Micrositeshrub -3.0529 1.9574 -1.560
## Pr(>|t|)
## (Intercept) 6.65e-08 ***
## poly(arid.gradient, 2)1 0.0363 *
## poly(arid.gradient, 2)2 0.4293
## Micrositeshrub 0.2637
## poly(arid.gradient, 2)1:Micrositeshrub 0.1980
## poly(arid.gradient, 2)2:Micrositeshrub 0.1331
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9787 on 22 degrees of freedom
## Multiple R-squared: 0.4229, Adjusted R-squared: 0.2918
## F-statistic: 3.225 on 5 and 22 DF, p-value: 0.0247ggplot(mean.spp) + geom_jitter(aes(x=arid.gradient, y=ihs(native), fill=Microsite, color=Microsite), size=3)+
scale_color_manual(values=c("#E69F00","#56B4E9"))+ stat_smooth(method="lm", formula= y~poly(x,2),aes(x=arid.gradient, y=ihs(native)), color="black")+ ylab("Native abundance") + xlab("aridity gradient")+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.line = element_line(colour = "black"),panel.background = element_blank(),axis.text=element_text(size=14),axis.title=element_text(size=16)) 
## non-native
m1 <- lm(ihs(non.native) ~ poly(arid.gradient,2), data=subset(mean.spp, Microsite=="shrub"))
summary(m1)##
## Call:
## lm(formula = ihs(non.native) ~ poly(arid.gradient, 2), data = subset(mean.spp,
## Microsite == "shrub"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.32477 -0.20348 -0.05303 0.53297 2.27555
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.0777 0.3340 9.214 1.66e-06 ***
## poly(arid.gradient, 2)1 6.5874 1.2498 5.271 0.000264 ***
## poly(arid.gradient, 2)2 0.7721 1.2498 0.618 0.549292
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.25 on 11 degrees of freedom
## Multiple R-squared: 0.7191, Adjusted R-squared: 0.668
## F-statistic: 14.08 on 2 and 11 DF, p-value: 0.0009268m1 <- lm(ihs(non.native) ~ poly(arid.gradient,2), data=subset(mean.spp, Microsite=="open"))
summary(m1)##
## Call:
## lm(formula = ihs(non.native) ~ poly(arid.gradient, 2), data = subset(mean.spp,
## Microsite == "open"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.66301 -0.60211 0.01432 0.55858 1.91804
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.8844 0.2828 10.200 6.06e-07 ***
## poly(arid.gradient, 2)1 6.1497 1.0581 5.812 0.000117 ***
## poly(arid.gradient, 2)2 0.4615 1.0581 0.436 0.671141
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.058 on 11 degrees of freedom
## Multiple R-squared: 0.7554, Adjusted R-squared: 0.7109
## F-statistic: 16.98 on 2 and 11 DF, p-value: 0.0004331m1 <- lm(ihs(non.native) ~ poly(arid.gradient,2)*Microsite, data=mean.spp)
summary(m1)##
## Call:
## lm(formula = ihs(non.native) ~ poly(arid.gradient, 2) * Microsite,
## data = mean.spp)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.32477 -0.43001 -0.01113 0.58589 2.27555
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 2.8844 0.3095 9.320
## poly(arid.gradient, 2)1 8.6970 1.6376 5.311
## poly(arid.gradient, 2)2 0.6527 1.6376 0.399
## Micrositeshrub 0.1933 0.4377 0.442
## poly(arid.gradient, 2)1:Micrositeshrub 0.6190 2.3159 0.267
## poly(arid.gradient, 2)2:Micrositeshrub 0.4393 2.3159 0.190
## Pr(>|t|)
## (Intercept) 4.28e-09 ***
## poly(arid.gradient, 2)1 2.49e-05 ***
## poly(arid.gradient, 2)2 0.694
## Micrositeshrub 0.663
## poly(arid.gradient, 2)1:Micrositeshrub 0.792
## poly(arid.gradient, 2)2:Micrositeshrub 0.851
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.158 on 22 degrees of freedom
## Multiple R-squared: 0.7361, Adjusted R-squared: 0.6761
## F-statistic: 12.27 on 5 and 22 DF, p-value: 9.2e-06m1 <- lm(ihs(non.native) ~ arid.gradient*Microsite, data=mean.spp)
summary(m1)##
## Call:
## lm(formula = ihs(non.native) ~ arid.gradient * Microsite, data = mean.spp)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.5300 -0.4742 0.1206 0.7847 2.0747
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.3299 1.0394 8.014 3.06e-08 ***
## arid.gradient 1.8530 0.3386 5.472 1.26e-05 ***
## Micrositeshrub 0.5808 1.4699 0.395 0.696
## arid.gradient:Micrositeshrub 0.1319 0.4789 0.275 0.785
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.124 on 24 degrees of freedom
## Multiple R-squared: 0.7289, Adjusted R-squared: 0.695
## F-statistic: 21.51 on 3 and 24 DF, p-value: 5.506e-07ggplot(mean.spp) + geom_jitter(aes(x=arid.gradient, y=ihs(non.native), fill=Microsite, color=Microsite), size=3)+
scale_color_manual(values=c("#E69F00","#56B4E9"))+ stat_smooth(method="lm", formula= y~x,aes(x=arid.gradient, y=ihs(non.native)), color="black")+ ylab("Non-native abundance") + xlab("aridity gradient")+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.line = element_line(colour = "black"),panel.background = element_blank(),axis.text=element_text(size=14),axis.title=element_text(size=16)) 
Phylogenetics
library(picante)
library(ape)
library(brranching)
library(taxize)
## load species list to create tree
spp.list <- read.csv("Data/ERG.specieslist.csv")
## create a combined species name column and drop erinoginum because not to species level
taxa <- paste(spp.list$Genus, spp.list$Species.name)
taxa <- taxa[taxa!="Erinoginum spp"]
## create tree of phylogeny
tree <- phylomatic(taxa=taxa, get = 'POST')
## calculate branch lengths using Grafen's method
## Grafen, A. (1989) The phylogenetic regression. Philosophical Transactions of the Royal society of London. Series B. Biological Sciences, 326, 119–157.
tree2 <- compute.brlen(tree, method="Grafen")
## view tree and outputs to verify branches
plot(tree2)
tree2##
## Phylogenetic tree with 39 tips and 29 internal nodes.
##
## Tip labels:
## cryptantha_barbigera, cryptantha_intermedia, phacelia_crenulata, phacelia_tanacetifolia, pectocarya_spp, amsinckia_grandiflora, ...
## Node labels:
## poales_to_asterales, eudicots, , , asterids, ericales_to_asterales, ...
##
## Rooted; includes branch lengths.## format community to only have site and microsite
comm <- community %>% group_by(Year, Site, Microsite) %>% summarise_if(is.numeric, funs(sum(., na.rm=T)))
comm <- comm[,c(1:3,13:53)] ## extract species abundances only
comm <- data.frame(comm) ## convert to dataframe
comm[,"site.micro"] <- paste(comm$Site,comm$Microsite,as.character(comm$Year)) ## create site by microsite column
rownames(comm) <- comm[,length(comm)] ## replace row names with site by microsite
comm[,c("Year","Site","Microsite","Erinoginum.spp","Boraginaceae.sp.","site.micro")] <- NULL ## drop all columns but ones for analysis
## match species name format
spp.list[,"spp.name"] <- paste(spp.list$Genus, spp.list$Species.name)
colnames(comm) <- spp.list$spp.name[match(colnames(comm), spp.list$Species.shorthand)]
## match species format
names(comm) <- gsub(" ", "_", names(comm), fixed=T) ## underscores instead of spaces
names(comm) <- gsub("__", "_", names(comm), fixed=T) ## replace double underscores with one
names(comm) <- tolower(names(comm)) ## lower case names
## mean phylogenetic distance
mpd.data <- mpd(comm, cophenetic(tree2), abundance.weighted=T)
## format dataframe
mpd.data <- data.frame(Year=c(rep(2016,14),rep(2017,14)),Microsite=c("open","shrub"),site=rownames(comm), mpd=mpd.data)
mpd.data[,"Site"] <- gsub(" open", "", mpd.data[,"site"])
mpd.data[,"Site"] <- gsub(" shrub", "", mpd.data[,"Site"])
mpd.data[,"Site"] <- gsub(" 2016", "", mpd.data[,"Site"])
mpd.data[,"Site"] <- gsub(" 2017", "", mpd.data[,"Site"])
mpd.data[,"site"] <- NULL
mpd.data[is.na(mpd.data)] <- 0 ## barstow no plants
## getspecies richness for sites
spp.rich <- community %>% group_by(Year, Site, Microsite) %>% summarize(richness=mean(Richness))
spp.rich <- data.frame(spp.rich)
## join data
mpd.rich <- merge(mpd.data, spp.rich, by=c("Site","Microsite","Year"))
mpd.arid <- merge(mpd.rich, mean.spp, by=c("Site","Microsite","Year"))
## phylogenetic distance
mpd.arid <- mpd.arid[c(2,4:nrow(mpd.arid)),] ## drop barstow
m1 <- lm(mpd~arid.gradient*Microsite, data=mpd.arid)
summary(m1)##
## Call:
## lm(formula = mpd ~ arid.gradient * Microsite, data = mpd.arid)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.51050 -0.19590 0.06299 0.14972 0.71607
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.97618 0.30321 3.219 0.00395 **
## arid.gradient 0.04716 0.10394 0.454 0.65451
## Micrositeshrub -1.07891 0.42881 -2.516 0.01966 *
## arid.gradient:Micrositeshrub -0.30441 0.14700 -2.071 0.05031 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2938 on 22 degrees of freedom
## Multiple R-squared: 0.3146, Adjusted R-squared: 0.2211
## F-statistic: 3.365 on 3 and 22 DF, p-value: 0.03692ggplot(mpd.arid) + geom_jitter(aes(x=arid.gradient, y=mpd, fill=Microsite, color=Microsite), size=3) +
scale_color_manual(values=c("#E69F00","#56B4E9")) + stat_smooth(method="lm", formula= y~x,aes(x=arid.gradient, y=mpd), data=subset(mpd.arid, Microsite=="shrub"), color="#56B4E9")+ stat_smooth(method="lm", formula= y~x,aes(x=arid.gradient, y=mpd), data=subset(mpd.arid, Microsite=="open"), color="#E69F00")+ ylab("Phylogenetic Community Dissimilarity") + xlab("Aridity Gradient")+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.line = element_line(colour = "black"),panel.background = element_blank(),axis.text=element_text(size=14),axis.title=element_text(size=16)) 
m1 <- lm(mpd~arid.gradient, data=subset(mpd.arid, Microsite=="shrub"))
summary(m1)##
## Call:
## lm(formula = mpd ~ arid.gradient, data = subset(mpd.arid, Microsite ==
## "shrub"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.51050 -0.23112 0.03664 0.14204 0.71607
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.1027 0.3563 -0.288 0.778
## arid.gradient -0.2573 0.1222 -2.106 0.059 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3452 on 11 degrees of freedom
## Multiple R-squared: 0.2873, Adjusted R-squared: 0.2225
## F-statistic: 4.435 on 1 and 11 DF, p-value: 0.05899m2 <- lm(mpd~arid.gradient, data=subset(mpd.arid, Microsite=="open"))
summary(m2)##
## Call:
## lm(formula = mpd ~ arid.gradient, data = subset(mpd.arid, Microsite ==
## "open"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.38078 -0.09025 0.08952 0.15668 0.33783
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.97618 0.23852 4.093 0.00178 **
## arid.gradient 0.04716 0.08177 0.577 0.57575
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2311 on 11 degrees of freedom
## Multiple R-squared: 0.02935, Adjusted R-squared: -0.05889
## F-statistic: 0.3326 on 1 and 11 DF, p-value: 0.5758Changes in Mechanisms along gradient
### Nutrients
nutrient.mean <- nutrients.climate %>% group_by(Site, arid.gradient, microsite) %>% summarize(nitrogen=mean(N), phosphorus=mean(P), potassium=mean(K))
nutrient.mean <- data.frame(nutrient.mean)
## nitrogen
m1 <- lm(nitrogen ~ arid.gradient, data=subset(nutrient.mean, microsite=="open"))
summary(m1)##
## Call:
## lm(formula = nitrogen ~ arid.gradient, data = subset(nutrient.mean,
## microsite == "open"))
##
## Residuals:
## 1 3 5 7 9 11 13
## 1.62742 0.10479 -1.35216 0.97441 -0.05162 -0.76608 -0.53677
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.7141 1.5041 -1.804 0.1310
## arid.gradient -1.2879 0.4313 -2.986 0.0306 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.124 on 5 degrees of freedom
## Multiple R-squared: 0.6407, Adjusted R-squared: 0.5689
## F-statistic: 8.917 on 1 and 5 DF, p-value: 0.03058anova(m1)## Analysis of Variance Table
##
## Response: nitrogen
## Df Sum Sq Mean Sq F value Pr(>F)
## arid.gradient 1 11.263 11.263 8.9174 0.03058 *
## Residuals 5 6.315 1.263
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(nutrient.mean) + geom_jitter(aes(x=arid.gradient, y=nitrogen, fill=microsite, color=microsite), size=3)+
scale_color_manual(values=c("#E69F00","#56B4E9")) +
stat_smooth(method="lm", formula= y~x,aes(x=arid.gradient, y=nitrogen), color="#56B4E9", data=subset(nutrient.mean, microsite=="shrub")) +
stat_smooth(method="lm", formula= y~x,aes(x=arid.gradient, y=nitrogen), color="#E69F00", data=subset(nutrient.mean, microsite=="open"))+ ylab("Nitrogen") + xlab("Aridity Gradient")+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.line = element_line(colour = "black"),panel.background = element_blank(),axis.text=element_text(size=14),axis.title=element_text(size=16)) 
## phosphorus
m2 <- lm(phosphorus ~ arid.gradient * microsite, data=nutrient.mean)
summary(m2)##
## Call:
## lm(formula = phosphorus ~ arid.gradient * microsite, data = nutrient.mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.1684 -2.3472 0.6103 1.9735 7.0340
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.8933 5.3802 1.653 0.129
## arid.gradient 0.7555 1.5428 0.490 0.635
## micrositeshrub 10.5952 7.6088 1.392 0.194
## arid.gradient:micrositeshrub 2.0483 2.1819 0.939 0.370
##
## Residual standard error: 4.02 on 10 degrees of freedom
## Multiple R-squared: 0.3967, Adjusted R-squared: 0.2158
## F-statistic: 2.192 on 3 and 10 DF, p-value: 0.152anova(m2)## Analysis of Variance Table
##
## Response: phosphorus
## Df Sum Sq Mean Sq F value Pr(>F)
## arid.gradient 1 43.011 43.011 2.6614 0.1339
## microsite 1 49.031 49.031 3.0339 0.1122
## arid.gradient:microsite 1 14.243 14.243 0.8813 0.3700
## Residuals 10 161.610 16.161ggplot(nutrient.mean) + geom_jitter(aes(x=arid.gradient, y=phosphorus, fill=microsite, color=microsite), size=3)+
scale_color_manual(values=c("#E69F00","#56B4E9")) ## not significant
m3 <- lm(potassium ~ arid.gradient * microsite, data=nutrient.mean)
summary(m3)##
## Call:
## lm(formula = potassium ~ arid.gradient * microsite, data = nutrient.mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -160.67 -64.64 -14.22 64.63 206.92
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 290.78 145.57 1.998 0.0737 .
## arid.gradient 44.64 41.74 1.069 0.3101
## micrositeshrub 278.33 205.87 1.352 0.2062
## arid.gradient:micrositeshrub 56.24 59.03 0.953 0.3633
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 108.8 on 10 degrees of freedom
## Multiple R-squared: 0.4843, Adjusted R-squared: 0.3295
## F-statistic: 3.13 on 3 and 10 DF, p-value: 0.07444anova(m3)## Analysis of Variance Table
##
## Response: potassium
## Df Sum Sq Mean Sq F value Pr(>F)
## arid.gradient 1 71879 71879 6.0754 0.0334 *
## microsite 1 28476 28476 2.4069 0.1519
## arid.gradient:microsite 1 10736 10736 0.9074 0.3633
## Residuals 10 118313 11831
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## potassium
ggplot(nutrient.mean) + geom_jitter(aes(x=arid.gradient, y=potassium, fill=microsite, color=microsite), size=3)+
scale_color_manual(values=c("#E69F00","#56B4E9")) +
stat_smooth(method="lm", formula= y~x,aes(x=arid.gradient, y=potassium), color="black") 
#### Shrubs increase nitrogen availability at aridity extremes #### Shrubs do not effect potassium or phosphorus availability
se <- function(x) sd(na.omit(x))/sqrt(length(na.omit(x)))
smc <- census %>% group_by(Year, Site, Microsite, Census) %>% summarize(swc.avg=mean(swc), swc.se=se(swc))
smc.arid <- merge(smc, site.climate, by=c("Site","Year"))
## early swc
smc.early <- subset(smc.arid, Census=="emergence")
ggplot(smc.early) + geom_jitter(aes(x=arid.gradient, y=swc.avg, fill=Microsite, color=Microsite), size=3)+
scale_color_manual(values=c("#E69F00","#56B4E9"))+
stat_smooth(method="lm", formula= y~poly(x,2),aes(x=arid.gradient, y=swc.avg), color="black") + ylab("Percent soil Moisture Content (emergence)") + xlab("Aridity Gradient")+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.line = element_line(colour = "black"),panel.background = element_blank(),axis.text=element_text(size=14),axis.title=element_text(size=16)) 
m1 <- lm(swc.avg~ poly(arid.gradient,2) * Microsite, data=smc.early)
summary(m1)##
## Call:
## lm(formula = swc.avg ~ poly(arid.gradient, 2) * Microsite, data = smc.early)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.8989 -2.2340 0.1864 2.2093 8.7293
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 11.1650 1.3550 8.240
## poly(arid.gradient, 2)1 32.2475 7.1700 4.498
## poly(arid.gradient, 2)2 20.8675 7.1700 2.910
## Micrositeshrub -0.4679 1.9163 -0.244
## poly(arid.gradient, 2)1:Micrositeshrub -3.3904 10.1399 -0.334
## poly(arid.gradient, 2)2:Micrositeshrub -3.1002 10.1399 -0.306
## Pr(>|t|)
## (Intercept) 3.6e-08 ***
## poly(arid.gradient, 2)1 0.000179 ***
## poly(arid.gradient, 2)2 0.008109 **
## Micrositeshrub 0.809376
## poly(arid.gradient, 2)1:Micrositeshrub 0.741276
## poly(arid.gradient, 2)2:Micrositeshrub 0.762672
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.07 on 22 degrees of freedom
## Multiple R-squared: 0.699, Adjusted R-squared: 0.6306
## F-statistic: 10.22 on 5 and 22 DF, p-value: 3.647e-05## end swc
smc.end <- subset(smc.arid, Census=="end")
ggplot(smc.end) + geom_jitter(aes(x=arid.gradient, y=swc.avg, fill=Microsite, color=Microsite), size=3)+
scale_color_manual(values=c("#E69F00","#56B4E9"))+
stat_smooth(method="lm", formula= y~poly(x,2),aes(x=arid.gradient, y=swc.avg), color="black") + ylab("Percent soil Moisture Content (end)") + xlab("Aridity Gradient")+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.line = element_line(colour = "black"),panel.background = element_blank(),axis.text=element_text(size=14),axis.title=element_text(size=16)) 
m1 <- lm(swc.avg~ poly(arid.gradient,2) * Microsite, data=smc.end)
summary(m1)##
## Call:
## lm(formula = swc.avg ~ poly(arid.gradient, 2) * Microsite, data = smc.end)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.7611 -2.4087 0.0009 2.0666 9.3760
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 5.093 1.335 3.815
## poly(arid.gradient, 2)1 15.502 7.065 2.194
## poly(arid.gradient, 2)2 17.571 7.065 2.487
## Micrositeshrub -0.576 1.888 -0.305
## poly(arid.gradient, 2)1:Micrositeshrub -1.945 9.991 -0.195
## poly(arid.gradient, 2)2:Micrositeshrub -3.453 9.991 -0.346
## Pr(>|t|)
## (Intercept) 0.000946 ***
## poly(arid.gradient, 2)1 0.039073 *
## poly(arid.gradient, 2)2 0.020948 *
## Micrositeshrub 0.763211
## poly(arid.gradient, 2)1:Micrositeshrub 0.847426
## poly(arid.gradient, 2)2:Micrositeshrub 0.732914
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.996 on 22 degrees of freedom
## Multiple R-squared: 0.4604, Adjusted R-squared: 0.3377
## F-statistic: 3.754 on 5 and 22 DF, p-value: 0.01312Shrubs do not alter soil moisture availability
hobo.means <- HOBO.data %>% group_by(season, Microsite, Site) %>% summarize(temp.avg=mean(Temp),temp.var=var(Temp),temp.se=se(Temp),rh.avg=mean(RH, na.rm=T),rh.var=var(RH, na.rm=T),rh.se=se(RH),frost=(sum(na.omit(Temp)<0)/length(na.omit(Temp))*100),heat=(sum(na.omit(Temp)>30)/length(na.omit(Temp))*100))
hobo.means[,"Year"] <- c(rep(2016,14),rep(2017,14))
hobo.arid <- merge(hobo.means, site.climate, by=c("Site","Year"))
## temperature
ggplot(hobo.arid) + geom_jitter(aes(x=arid.gradient, y=temp.avg, fill=Microsite, color=Microsite), size=3)+
scale_color_manual(values=c("#E69F00","#56B4E9"))+
stat_smooth(method="lm", formula= y~poly(x,2),aes(x=arid.gradient, y=temp.avg), color="black") + ylab("Average Temperature (C°)") + xlab("Aridity Gradient")+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.line = element_line(colour = "black"),panel.background = element_blank(),axis.text=element_text(size=14),axis.title=element_text(size=16)) 
m1 <- lm(temp.avg~arid.gradient * Microsite, data=hobo.arid)
summary(m1)##
## Call:
## lm(formula = temp.avg ~ arid.gradient * Microsite, data = hobo.arid)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2362 -0.7204 -0.3884 0.4197 3.2981
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.21950 1.38277 4.498 0.000149 ***
## arid.gradient -1.75909 0.45045 -3.905 0.000669 ***
## Micrositeshrub -1.42763 1.95554 -0.730 0.472428
## arid.gradient:Micrositeshrub 0.00911 0.63704 0.014 0.988709
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.495 on 24 degrees of freedom
## Multiple R-squared: 0.6064, Adjusted R-squared: 0.5571
## F-statistic: 12.32 on 3 and 24 DF, p-value: 4.451e-05anova(m1)## Analysis of Variance Table
##
## Response: temp.avg
## Df Sum Sq Mean Sq F value Pr(>F)
## arid.gradient 1 67.815 67.815 30.3425 1.154e-05 ***
## Microsite 1 14.807 14.807 6.6252 0.01666 *
## arid.gradient:Microsite 1 0.000 0.000 0.0002 0.98871
## Residuals 24 53.639 2.235
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m1.s <- lm(temp.avg~arid.gradient, data=subset(hobo.arid, Microsite=="shrub"))
summary(m1.s)##
## Call:
## lm(formula = temp.avg ~ arid.gradient, data = subset(hobo.arid,
## Microsite == "shrub"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.5836 -0.9458 -0.4008 0.4122 3.2981
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.7919 1.3727 3.491 0.00446 **
## arid.gradient -1.7500 0.4472 -3.913 0.00206 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.484 on 12 degrees of freedom
## Multiple R-squared: 0.5607, Adjusted R-squared: 0.5241
## F-statistic: 15.31 on 1 and 12 DF, p-value: 0.00206## RH
ggplot(hobo.arid) + geom_jitter(aes(x=arid.gradient, y=rh.avg, fill=Microsite, color=Microsite), size=3)+
scale_color_manual(values=c("#E69F00","#56B4E9"))+
stat_smooth(method="lm", formula= y~exp(x),aes(x=arid.gradient, y=rh.avg), color="black") + ylab("Relative Humidity (%)") + xlab("Aridity Gradient")+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.line = element_line(colour = "black"),panel.background = element_blank(),axis.text=element_text(size=14),axis.title=element_text(size=16)) 
m2 <- lm(log(rh.avg)~arid.gradient * Microsite, data=hobo.arid)
summary(m2)##
## Call:
## lm(formula = log(rh.avg) ~ arid.gradient * Microsite, data = hobo.arid)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.313369 -0.094086 0.008993 0.111107 0.252701
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.703640 0.165465 28.427 < 2e-16 ***
## arid.gradient 0.251051 0.053902 4.658 9.92e-05 ***
## Micrositeshrub 0.049072 0.234003 0.210 0.836
## arid.gradient:Micrositeshrub 0.004987 0.076229 0.065 0.948
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1789 on 24 degrees of freedom
## Multiple R-squared: 0.6497, Adjusted R-squared: 0.6059
## F-statistic: 14.84 on 3 and 24 DF, p-value: 1.131e-05anova(m2)## Analysis of Variance Table
##
## Response: log(rh.avg)
## Df Sum Sq Mean Sq F value Pr(>F)
## arid.gradient 1 1.41614 1.41614 44.2510 7.014e-07 ***
## Microsite 1 0.00829 0.00829 0.2591 0.6154
## arid.gradient:Microsite 1 0.00014 0.00014 0.0043 0.9484
## Residuals 24 0.76806 0.03200
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## frost
hobo.arid2 <- subset(hobo.arid, Site!="Cuyama") ## drop cuyama because abnormal frost periods
ggplot(hobo.arid2) + geom_jitter(aes(x=arid.gradient, y=frost, fill=Microsite, color=Microsite), size=3)+
scale_color_manual(values=c("#E69F00","#56B4E9"))+
stat_smooth(method="lm", formula= y~x,aes(x=arid.gradient, y=frost), color="black") + ylab("Number of frost days") + xlab("Aridity Gradient")+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.line = element_line(colour = "black"),panel.background = element_blank(),axis.text=element_text(size=14),axis.title=element_text(size=16)) 
m3 <- lm(frost~arid.gradient * Microsite, data=hobo.arid2)
summary(m3)##
## Call:
## lm(formula = frost ~ arid.gradient * Microsite, data = hobo.arid2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2695 -1.3394 -0.3817 1.9954 3.3744
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.7896 2.2613 4.771 0.000116 ***
## arid.gradient 1.4999 0.7049 2.128 0.045970 *
## Micrositeshrub -2.3006 3.1980 -0.719 0.480218
## arid.gradient:Micrositeshrub -0.3756 0.9969 -0.377 0.710319
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.117 on 20 degrees of freedom
## Multiple R-squared: 0.3059, Adjusted R-squared: 0.2018
## F-statistic: 2.938 on 3 and 20 DF, p-value: 0.05818anova(m3)## Analysis of Variance Table
##
## Response: frost
## Df Sum Sq Mean Sq F value Pr(>F)
## arid.gradient 1 31.066 31.0655 6.9304 0.01596 *
## Microsite 1 7.806 7.8058 1.7414 0.20187
## arid.gradient:Microsite 1 0.636 0.6363 0.1419 0.71032
## Residuals 20 89.649 4.4825
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## temperature variability
ggplot(hobo.arid) + geom_jitter(aes(x=arid.gradient, y=temp.var.x, fill=Microsite, color=Microsite), size=3)+
scale_color_manual(values=c("#E69F00","#56B4E9"))+
stat_smooth(method="lm", formula= y~exp(x),aes(x=arid.gradient, y=temp.var.x), color="#E69F00", data=subset(hobo.arid, Microsite=="open")) + ylab("Number of frost days")+
stat_smooth(method="lm", formula= y~exp(x),aes(x=arid.gradient, y=temp.var.x), color="#56B4E9", data=subset(hobo.arid, Microsite=="shrub")) + ylab("temperature variation") + xlab("Aridity Gradient")+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.line = element_line(colour = "black"),panel.background = element_blank(),axis.text=element_text(size=14),axis.title=element_text(size=16)) 
m4 <- lm(log(temp.var.x)~arid.gradient * Microsite, data=hobo.arid)
summary(m4)##
## Call:
## lm(formula = log(temp.var.x) ~ arid.gradient * Microsite, data = hobo.arid)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.75540 -0.15564 0.02433 0.17182 0.46013
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.83222 0.26061 14.705 1.67e-13 ***
## arid.gradient -0.19133 0.08490 -2.254 0.0336 *
## Micrositeshrub -0.46416 0.36856 -1.259 0.2200
## arid.gradient:Micrositeshrub -0.02535 0.12006 -0.211 0.8346
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2818 on 24 degrees of freedom
## Multiple R-squared: 0.51, Adjusted R-squared: 0.4488
## F-statistic: 8.327 on 3 and 24 DF, p-value: 0.0005707anova(m4)## Analysis of Variance Table
##
## Response: log(temp.var.x)
## Df Sum Sq Mean Sq F value Pr(>F)
## arid.gradient 1 0.91677 0.91677 11.5480 0.002368 **
## Microsite 1 1.06294 1.06294 13.3892 0.001241 **
## arid.gradient:Microsite 1 0.00354 0.00354 0.0446 0.834588
## Residuals 24 1.90531 0.07939
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## extreme heat
## reverse inverse hyperbolic sine
hs <- function(x) {
y <- 0.5*exp(-x)*(exp(2*x)-1)
return(y)
}
ggplot(hobo.arid) + geom_jitter(aes(x=arid.gradient, y=heat, fill=Microsite, color=Microsite), size=3)+
scale_color_manual(values=c("#E69F00","#56B4E9"))+
stat_smooth(method="lm", formula= y~hs(x),aes(x=arid.gradient, y=heat), color="#E69F00", data=subset(hobo.arid, Microsite=="open")) +
stat_smooth(method="lm", formula= y~hs(x),aes(x=arid.gradient, y=heat), color="#56B4E9", data=subset(hobo.arid, Microsite=="shrub")) + ylab("extreme heat days variation") + xlab("Aridity Gradient")+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.line = element_line(colour = "black"),panel.background = element_blank(),axis.text=element_text(size=14),axis.title=element_text(size=16)) 
m5 <- lm(ihs(heat)~arid.gradient * Microsite, data=hobo.arid)
summary(m5)##
## Call:
## lm(formula = ihs(heat) ~ arid.gradient * Microsite, data = hobo.arid)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.7532 -0.3832 -0.1102 0.2072 1.2432
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.2199 0.4899 -0.449 0.657489
## arid.gradient -0.7396 0.1596 -4.635 0.000105 ***
## Micrositeshrub -0.1480 0.6928 -0.214 0.832615
## arid.gradient:Micrositeshrub 0.1787 0.2257 0.792 0.436163
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5296 on 24 degrees of freedom
## Multiple R-squared: 0.6529, Adjusted R-squared: 0.6095
## F-statistic: 15.05 on 3 and 24 DF, p-value: 1.015e-05anova(m5)## Analysis of Variance Table
##
## Response: ihs(heat)
## Df Sum Sq Mean Sq F value Pr(>F)
## arid.gradient 1 9.3144 9.3144 33.2088 6.124e-06 ***
## Microsite 1 3.1726 3.1726 11.3114 0.00258 **
## arid.gradient:Microsite 1 0.1759 0.1759 0.6271 0.43616
## Residuals 24 6.7315 0.2805
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1shrubs reduce temperature variability
shrubz <- read.csv("Data/ERG.shrub.csv")
shrub.mean <- shrubz %>% group_by(Site, Microsite) %>% summarize(compact=mean(Compaction))
shrub.clim <- merge(shrub.mean, site.climate, by=c("Site"))
#shrub.clim <- subset(shrub.clim, Year==2016)
ggplot(shrub.clim) + geom_jitter(aes(x=arid.gradient, y=compact, fill=Microsite, color=Microsite), size=3)+
scale_color_manual(values=c("#E69F00","#56B4E9"))+ stat_smooth(method="lm", formula= y~exp(x),aes(x=arid.gradient, y=compact), color="#E69F00", data=subset(shrub.clim, Microsite=="open"))+
stat_smooth(method="lm", formula= y~exp(x),aes(x=arid.gradient, y=compact), color="#56B4E9", data=subset(shrub.clim, Microsite=="shrub")) + ylab("soil compaction") + xlab("Aridity Gradient")+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.line = element_line(colour = "black"),panel.background = element_blank(),axis.text=element_text(size=14),axis.title=element_text(size=16)) 
m1 <- lm(log(compact) ~ arid.gradient* Microsite, data=shrub.clim)
summary(m1)##
## Call:
## lm(formula = log(compact) ~ arid.gradient * Microsite, data = shrub.clim)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.36122 -0.13659 -0.02823 0.05479 0.61368
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.26645 0.23301 1.144 0.26410
## arid.gradient -0.07328 0.07590 -0.965 0.34394
## Micrositeshrub -1.19983 0.32952 -3.641 0.00130 **
## arid.gradient:Micrositeshrub -0.32933 0.10734 -3.068 0.00528 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2519 on 24 degrees of freedom
## Multiple R-squared: 0.5932, Adjusted R-squared: 0.5424
## F-statistic: 11.67 on 3 and 24 DF, p-value: 6.53e-05anova(m1)## Analysis of Variance Table
##
## Response: log(compact)
## Df Sum Sq Mean Sq F value Pr(>F)
## arid.gradient 1 1.24730 1.24730 19.6548 0.0001755 ***
## Microsite 1 0.37672 0.37672 5.9362 0.0226248 *
## arid.gradient:Microsite 1 0.59733 0.59733 9.4127 0.0052769 **
## Residuals 24 1.52305 0.06346
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1shrubs reduce soil compaction and increase substrate penetration
Phytometer models
library(lsmeans)## The 'lsmeans' package is being deprecated.
## Users are encouraged to switch to 'emmeans'.
## See help('transition') for more information, including how
## to convert 'lsmeans' objects and scripts to work with 'emmeans'.census.arid <- merge(census.end, site.climate, by=c("Year","Site"))
## all species end of season
m1 <- glm.nb(phyto.abd~ Microsite * Nutrient * arid.gradient, data=census.arid)
anova(m2, test="Chisq") ## Site and microsite*nutrient significant## Analysis of Variance Table
##
## Response: log(rh.avg)
## Df Sum Sq Mean Sq F value Pr(>F)
## arid.gradient 1 1.41614 1.41614 44.2510 7.014e-07 ***
## Microsite 1 0.00829 0.00829 0.2591 0.6154
## arid.gradient:Microsite 1 0.00014 0.00014 0.0043 0.9484
## Residuals 24 0.76806 0.03200
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## run full model for phacelia biomass
m1 <- aov(log(Phacelia.biomass)~ Microsite * Nutrient * arid.gradient, data=subset(census.arid, Phacelia.biomass>0))
summary(m1) ## site and microsite## Df Sum Sq Mean Sq F value Pr(>F)
## Microsite 1 19.0 19.00 9.126 0.00281 **
## Nutrient 1 8.8 8.76 4.207 0.04141 *
## arid.gradient 1 122.7 122.66 58.927 4.95e-13 ***
## Microsite:Nutrient 1 0.2 0.16 0.078 0.78078
## Microsite:arid.gradient 1 1.4 1.41 0.678 0.41121
## Nutrient:arid.gradient 1 0.0 0.00 0.000 0.99128
## Microsite:Nutrient:arid.gradient 1 2.3 2.27 1.088 0.29795
## Residuals 225 468.3 2.08
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(m1, "Microsite")## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## arid.gradient## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## Microsite, arid.gradient## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## Nutrient, arid.gradient## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## Microsite, Nutrient, arid.gradient## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = log(Phacelia.biomass) ~ Microsite * Nutrient * arid.gradient, data = subset(census.arid, Phacelia.biomass > 0))
##
## $Microsite
## diff lwr upr p adj
## shrub-open 0.5995862 0.2084783 0.9906941 0.0028111TukeyHSD(m1, "Nutrient")## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## arid.gradient## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## Microsite, arid.gradient## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## Nutrient, arid.gradient## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## Microsite, Nutrient, arid.gradient## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = log(Phacelia.biomass) ~ Microsite * Nutrient * arid.gradient, data = subset(census.arid, Phacelia.biomass > 0))
##
## $Nutrient
## diff lwr upr p adj
## Ambient-Added -0.3865601 -0.7590709 -0.01404918 0.0420304## run full model for plantago biomass
m2 <- aov(log(Plantago.biomass)~ Microsite * Nutrient * arid.gradient, data=subset(census.arid, Plantago.biomass>0))
summary(m2) ## site and year + sitexmicrosite## Df Sum Sq Mean Sq F value Pr(>F)
## Microsite 1 9.59 9.59 5.718 0.018067 *
## Nutrient 1 26.14 26.14 15.582 0.000122 ***
## arid.gradient 1 136.39 136.39 81.293 1.01e-15 ***
## Microsite:Nutrient 1 0.61 0.61 0.366 0.546337
## Microsite:arid.gradient 1 1.57 1.57 0.937 0.334540
## Nutrient:arid.gradient 1 3.96 3.96 2.362 0.126491
## Microsite:Nutrient:arid.gradient 1 3.21 3.21 1.912 0.168873
## Residuals 146 244.96 1.68
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(m2, "Microsite")## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## arid.gradient## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## Microsite, arid.gradient## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## Nutrient, arid.gradient## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## Microsite, Nutrient, arid.gradient## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = log(Plantago.biomass) ~ Microsite * Nutrient * arid.gradient, data = subset(census.arid, Plantago.biomass > 0))
##
## $Microsite
## diff lwr upr p adj
## shrub-open -0.5018942 -0.9167114 -0.08707699 0.0180668TukeyHSD(m2, "Nutrient")## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## arid.gradient## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## Microsite, arid.gradient## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## Nutrient, arid.gradient## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## Microsite, Nutrient, arid.gradient## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = log(Plantago.biomass) ~ Microsite * Nutrient * arid.gradient, data = subset(census.arid, Plantago.biomass > 0))
##
## $Nutrient
## diff lwr upr p adj
## Ambient-Added -0.8205043 -1.23339 -0.4076185 0.0001319## run full model for salvia biomass
m3 <- aov(log(Salvia.biomass)~ Microsite * Nutrient * arid.gradient, data=subset(census.arid, Salvia.biomass>0))
summary(m3) ## site and year + sitexmicrosite## Df Sum Sq Mean Sq F value Pr(>F)
## Microsite 1 0.1 0.11 0.054 0.815771
## Nutrient 1 24.8 24.84 12.494 0.000465 ***
## arid.gradient 1 33.7 33.73 16.965 4.79e-05 ***
## Microsite:Nutrient 1 0.2 0.23 0.115 0.734281
## Microsite:arid.gradient 1 0.6 0.59 0.299 0.585029
## Nutrient:arid.gradient 1 20.3 20.28 10.199 0.001538 **
## Microsite:Nutrient:arid.gradient 1 0.0 0.00 0.000 0.988929
## Residuals 338 672.1 1.99
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(m3, "Microsite")## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## arid.gradient## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## Microsite, arid.gradient## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## Nutrient, arid.gradient## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## Microsite, Nutrient, arid.gradient## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = log(Salvia.biomass) ~ Microsite * Nutrient * arid.gradient, data = subset(census.arid, Salvia.biomass > 0))
##
## $Microsite
## diff lwr upr p adj
## shrub-open 0.03535378 -0.26289 0.3335975 0.8157715m3.n <- lm(log(Salvia.biomass)~ arid.gradient, data=subset(census.arid, Salvia.biomass>0 & Nutrient =="Added"))
m3.a <- lm(log(Salvia.biomass)~ arid.gradient, data=subset(census.arid, Salvia.biomass>0 & Nutrient =="Ambient"))
## abundance
## run full model for phacelia abundance
m1 <- glm.nb(Phacelia~ Microsite * Nutrient * arid.gradient, data=census.arid)
summary(m1) ## site and microsite##
## Call:
## glm.nb(formula = Phacelia ~ Microsite * Nutrient * arid.gradient,
## data = census.arid, init.theta = 0.2308296117, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.03830 -0.81531 -0.76103 0.01823 3.03660
##
## Coefficients:
## Estimate Std. Error z value
## (Intercept) -0.0007756 0.5883363 -0.001
## Micrositeshrub 1.2705913 0.8115605 1.566
## NutrientAmbient 0.1077404 0.8333728 0.129
## arid.gradient 0.1858087 0.1944620 0.956
## Micrositeshrub:NutrientAmbient 0.1849576 1.1409892 0.162
## Micrositeshrub:arid.gradient 0.2621592 0.2696848 0.972
## NutrientAmbient:arid.gradient 0.0421013 0.2759568 0.153
## Micrositeshrub:NutrientAmbient:arid.gradient -0.0505405 0.3789622 -0.133
## Pr(>|z|)
## (Intercept) 0.999
## Micrositeshrub 0.117
## NutrientAmbient 0.897
## arid.gradient 0.339
## Micrositeshrub:NutrientAmbient 0.871
## Micrositeshrub:arid.gradient 0.331
## NutrientAmbient:arid.gradient 0.879
## Micrositeshrub:NutrientAmbient:arid.gradient 0.894
##
## (Dispersion parameter for Negative Binomial(0.2308) family taken to be 1)
##
## Null deviance: 585.87 on 839 degrees of freedom
## Residual deviance: 550.93 on 832 degrees of freedom
## AIC: 1941.5
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 0.2308
## Std. Err.: 0.0231
##
## 2 x log-likelihood: -1923.4540anova(m1, test="Chisq")## Warning in anova.negbin(m1, test = "Chisq"): tests made without re-
## estimating 'theta'## Analysis of Deviance Table
##
## Model: Negative Binomial(0.2308), link: log
##
## Response: Phacelia
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev
## NULL 839 585.87
## Microsite 1 19.9145 838 565.96
## Nutrient 1 1.0729 837 564.89
## arid.gradient 1 11.4485 836 553.44
## Microsite:Nutrient 1 1.0254 835 552.41
## Microsite:arid.gradient 1 1.4563 834 550.96
## Nutrient:arid.gradient 1 0.0064 833 550.95
## Microsite:Nutrient:arid.gradient 1 0.0167 832 550.93
## Pr(>Chi)
## NULL
## Microsite 8.098e-06 ***
## Nutrient 0.3002852
## arid.gradient 0.0007155 ***
## Microsite:Nutrient 0.3112354
## Microsite:arid.gradient 0.2275227
## Nutrient:arid.gradient 0.9362121
## Microsite:Nutrient:arid.gradient 0.8971935
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1lsmeans(m1, pairwise~"Microsite")## NOTE: Results may be misleading due to involvement in interactions## $lsmeans
## Microsite lsmean SE df asymp.LCL asymp.UCL
## open -0.5548206 0.1205597 NA -0.7911133 -0.3185280
## shrub 0.1120837 0.1124787 NA -0.1083705 0.3325379
##
## Results are averaged over the levels of: Nutrient
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## open - shrub -0.6669043 0.1648821 NA -4.045 0.0001
##
## Results are averaged over the levels of: Nutrient
## Results are given on the log (not the response) scale.## run full model for plantago biomass
m2 <- glm.nb(Plantago~ Microsite * Nutrient * arid.gradient, data=census.arid)
summary(m2) ## site and microsite##
## Call:
## glm.nb(formula = Plantago ~ Microsite * Nutrient * arid.gradient,
## data = census.arid, init.theta = 0.1287536289, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.8053 -0.7211 -0.6578 -0.5689 2.4129
##
## Coefficients:
## Estimate Std. Error z value
## (Intercept) 1.0031515 0.7094622 1.414
## Micrositeshrub -2.8208986 1.0387678 -2.716
## NutrientAmbient -0.0810403 1.0160835 -0.080
## arid.gradient 0.3420451 0.2337751 1.463
## Micrositeshrub:NutrientAmbient 0.5580731 1.4672180 0.380
## Micrositeshrub:arid.gradient -0.7058890 0.3368271 -2.096
## NutrientAmbient:arid.gradient 0.0826214 0.3361349 0.246
## Micrositeshrub:NutrientAmbient:arid.gradient -0.0006522 0.4776644 -0.001
## Pr(>|z|)
## (Intercept) 0.15737
## Micrositeshrub 0.00662 **
## NutrientAmbient 0.93643
## arid.gradient 0.14343
## Micrositeshrub:NutrientAmbient 0.70368
## Micrositeshrub:arid.gradient 0.03611 *
## NutrientAmbient:arid.gradient 0.80584
## Micrositeshrub:NutrientAmbient:arid.gradient 0.99891
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(0.1288) family taken to be 1)
##
## Null deviance: 427.38 on 839 degrees of freedom
## Residual deviance: 414.78 on 832 degrees of freedom
## AIC: 1596.1
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 0.1288
## Std. Err.: 0.0138
##
## 2 x log-likelihood: -1578.1100anova(m2, test="Chisq")## Warning in anova.negbin(m2, test = "Chisq"): tests made without re-
## estimating 'theta'## Analysis of Deviance Table
##
## Model: Negative Binomial(0.1288), link: log
##
## Response: Plantago
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 839 427.38
## Microsite 1 5.5584 838 421.82 0.01839
## Nutrient 1 0.0296 837 421.79 0.86334
## arid.gradient 1 0.5610 836 421.23 0.45386
## Microsite:Nutrient 1 1.6621 835 419.56 0.19732
## Microsite:arid.gradient 1 4.7218 834 414.84 0.02978
## Nutrient:arid.gradient 1 0.0649 833 414.78 0.79889
## Microsite:Nutrient:arid.gradient 1 0.0000 832 414.78 0.99859
##
## NULL
## Microsite *
## Nutrient
## arid.gradient
## Microsite:Nutrient
## Microsite:arid.gradient *
## Nutrient:arid.gradient
## Microsite:Nutrient:arid.gradient
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1lsmeans(m2, pairwise~"Microsite")## NOTE: Results may be misleading due to involvement in interactions## $lsmeans
## Microsite lsmean SE df asymp.LCL asymp.UCL
## open -0.1639688 0.1466943 NA -0.4514843 0.1235467
## shrub -0.6304166 0.1522519 NA -0.9288248 -0.3320084
##
## Results are averaged over the levels of: Nutrient
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## open - shrub 0.4664478 0.2114234 NA 2.206 0.0274
##
## Results are averaged over the levels of: Nutrient
## Results are given on the log (not the response) scale.## run full model for salvia biomass
m3 <- glm.nb(Salvia~ Microsite * Nutrient * arid.gradient, data=census.arid)
summary(m3) ## site and microsite##
## Call:
## glm.nb(formula = Salvia ~ Microsite * Nutrient * arid.gradient,
## data = census.arid, init.theta = 0.3061627656, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.3585 -1.1226 -0.8020 0.2021 2.2024
##
## Coefficients:
## Estimate Std. Error z value
## (Intercept) 2.39712 0.46918 5.109
## Micrositeshrub -0.02997 0.67161 -0.045
## NutrientAmbient 0.61208 0.67236 0.910
## arid.gradient 0.57763 0.15655 3.690
## Micrositeshrub:NutrientAmbient 0.16062 0.94881 0.169
## Micrositeshrub:arid.gradient 0.07096 0.22516 0.315
## NutrientAmbient:arid.gradient 0.24763 0.22629 1.094
## Micrositeshrub:NutrientAmbient:arid.gradient -0.14470 0.31899 -0.454
## Pr(>|z|)
## (Intercept) 3.24e-07 ***
## Micrositeshrub 0.964406
## NutrientAmbient 0.362646
## arid.gradient 0.000225 ***
## Micrositeshrub:NutrientAmbient 0.865571
## Micrositeshrub:arid.gradient 0.752644
## NutrientAmbient:arid.gradient 0.273802
## Micrositeshrub:NutrientAmbient:arid.gradient 0.650108
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(0.3062) family taken to be 1)
##
## Null deviance: 785.98 on 839 degrees of freedom
## Residual deviance: 721.96 on 832 degrees of freedom
## AIC: 2994
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 0.3062
## Std. Err.: 0.0230
##
## 2 x log-likelihood: -2976.0340anova(m3, test="Chisq")## Warning in anova.negbin(m3, test = "Chisq"): tests made without re-
## estimating 'theta'## Analysis of Deviance Table
##
## Model: Negative Binomial(0.3062), link: log
##
## Response: Salvia
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 839 785.98
## Microsite 1 0.258 838 785.72 0.61139
## Nutrient 1 3.609 837 782.11 0.05747
## arid.gradient 1 54.596 836 727.51 1.48e-13
## Microsite:Nutrient 1 4.529 835 722.98 0.03332
## Microsite:arid.gradient 1 0.005 834 722.98 0.94599
## Nutrient:arid.gradient 1 0.870 833 722.11 0.35089
## Microsite:Nutrient:arid.gradient 1 0.145 832 721.96 0.70327
##
## NULL
## Microsite
## Nutrient .
## arid.gradient ***
## Microsite:Nutrient *
## Microsite:arid.gradient
## Nutrient:arid.gradient
## Microsite:Nutrient:arid.gradient
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1lsmeans(m3, pairwise~Microsite*Nutrient)## NOTE: Results may be misleading due to involvement in interactions## $lsmeans
## Microsite Nutrient lsmean SE df asymp.LCL asymp.UCL
## open Added 0.6995864 0.1351555 NA 0.4346865 0.9644863
## shrub Added 0.4610742 0.1382722 NA 0.1900656 0.7320828
## open Ambient 0.5839160 0.1380287 NA 0.3133848 0.8544472
## shrub Ambient 0.9312613 0.1338667 NA 0.6688874 1.1936351
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## open,Added - shrub,Added 0.2385122 0.1933552 NA 1.234 0.6054
## open,Added - open,Ambient 0.1156704 0.1931811 NA 0.599 0.9325
## open,Added - shrub,Ambient -0.2316749 0.1902296 NA -1.218 0.6154
## shrub,Added - open,Ambient -0.1228418 0.1953743 NA -0.629 0.9228
## shrub,Added - shrub,Ambient -0.4701871 0.1924565 NA -2.443 0.0692
## open,Ambient - shrub,Ambient -0.3473453 0.1922816 NA -1.806 0.2702
##
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 4 estimates## treatment plots
census.long <- gather(census.arid, species, biomass, 12:14)
census.long[,"species"] <- as.factor(census.long$species)
## microsite average
census.micro <- census.long %>% filter(biomass>0) %>% group_by(Microsite, species) %>% summarize(Biomass=mean(biomass), error=se(biomass))
ggplot(census.micro, aes(x=species, y=Biomass, fill=Microsite)) + geom_bar(position=position_dodge(), stat="identity", colour="black") + scale_fill_brewer()+ geom_errorbar(aes(ymin=Biomass-error, ymax=Biomass+error),
width=.2, # Width of the error bars
position=position_dodge(.9))+ theme_Publication()## Warning: `legend.margin` must be specified using `margin()`. For the old
## behavior use legend.spacing## Warning in grid.Call(L_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
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## nutrient average
census.nut <- census.long %>% filter(biomass>0) %>% group_by(Nutrient, species) %>% summarize(Biomass=mean(biomass), error=se(biomass))
ggplot(census.nut, aes(x=species, y=Biomass, fill=Nutrient)) + geom_bar(position=position_dodge(), stat="identity",colour="black") + scale_fill_brewer()+ geom_errorbar(aes(ymin=Biomass-error, ymax=Biomass+error),
width=.2, # Width of the error bars
position=position_dodge(.9))+ theme_Publication()## Warning: `legend.margin` must be specified using `margin()`. For the old
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## aridity average
ggplot(census.long, aes(x=arid.gradient, y=biomass, fill=species, colour=species)) + theme_Publication()+ stat_smooth(method="lm", formula= y~exp(x),aes(x=arid.gradient, y=biomass), color="#009E73", fill="#009E7340", data=subset(census.long, species=="Phacelia.biomass" & biomass>0)) + stat_smooth(method="lm", formula= y~exp(x),aes(x=arid.gradient, y=biomass), color="#E69F00",fill="#E69F0040", data=subset(census.long, species=="Plantago.biomass" & biomass>0))+ stat_smooth(method="lm", formula= y~exp(x),aes(x=arid.gradient, y=biomass), color="#56B4E9", fill="#56B4E940", data=subset(census.long, species=="Salvia.biomass" & biomass>0))## Warning: `legend.margin` must be specified using `margin()`. For the old
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m1 <- lm(log(biomass)~arid.gradient, data=subset(census.long, species=="Phacelia.biomass" & biomass >0))
m2 <- lm(log(biomass)~arid.gradient, data=subset(census.long, species=="Plantago.biomass" & biomass>0))
m3 <- lm(log(biomass)~arid.gradient, data=subset(census.long, species=="Salvia.biomass" & biomass>0))Community analysis
## Regressions To Separate Phylogenetic Attraction And Repulsion
## sppregs(community[,13:53], )
### Network analysis
library(igraph)
library(statnet)
## reload native vs non-native species
status <- read.csv("Data//ERG.specieslist.csv")
shrub.comm <- subset(community, Microsite=="shrub", select=13:53)
shrub.comm <- shrub.comm[,colSums(shrub.comm)!=0]
open.comm <- subset(community, Microsite=="open", select=13:53)
open.comm <- open.comm[,colSums(open.comm)!=0]
## Shrub
## correlation species network
network <- data.frame(cor(shrub.comm))
network[,"To"] <- colnames(network)
test <- gather(network, From, correlation, 1:(ncol(network)-1))
test <- subset(test, correlation>0.10 & correlation != 1 | correlation < -0.10 )
n.shrub <- graph_from_data_frame(test[,1:2], directed=TRUE)
## plot network circle
lo <- layout_in_circle(n.shrub)
plot(n.shrub, layout=lo)
transitivity(n.shrub, type = "local") ## [1] 0.16666667 0.06060606 0.13333333 0.08888889 0.00000000 0.03296703
## [7] 0.07575758 0.10714286 0.03684211 0.06535948 0.00000000 0.07142857
## [13] 0.07142857 0.07142857 0.02222222 0.00000000 0.00000000 0.16666667
## [19] 0.00000000 0.00000000 0.10714286 0.16666667 0.00000000 0.13333333
## [25] 0.06666667 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## [31] 0.00000000transitivity(n.shrub, type = "global") ## [1] 0.2755102## colour graphs
shapes <- V(n.shrub)$name %in% (subset(status, status=="non.native", select=1))[,1] +1
plot(n.shrub, layout=layout_on_grid(n.shrub),vertex.shape=c("circle", "square")[shapes], vertex.color=c("tomato2", "royalblue")[shapes])
## open
## correlation species network
network <- data.frame(cor(open.comm))
network[,"To"] <- colnames(network)
test <- gather(network, From, correlation, 1:(ncol(network)-1))
test <- subset(test, correlation>0.10 & correlation != 1 | correlation < -0.10 )
n.open <- graph_from_data_frame(test[,1:2], directed=TRUE)
## plot network circle
lo <- layout_in_circle(n.open)
plot(n.open, layout=lo)
## colour graphs
shapes <- V(n.open)$name %in% (subset(status, status=="non.native", select=1))[,1] +1
plot(n.open, layout=layout_on_grid(n.open),vertex.shape=c("circle", "square")[shapes], vertex.color=c("tomato2", "royalblue")[shapes])
## statistics
transitivity(n.open, type = "local") ## [1] 0.09890110 0.17857143 0.03333333 0.06666667 0.16666667 0.03921569
## [7] 0.03030303 0.20000000 0.20000000 0.07189542 0.00000000 0.00000000
## [13] 0.00000000 0.00000000 0.06666667 0.03571429 0.06666667 0.06666667
## [19] 0.06666667 0.00000000 0.16666667 0.00000000 0.16666667 0.16666667
## [25] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.16666667
## [31] 0.00000000 0.00000000 0.00000000transitivity(n.open, type = "global") ## [1] 0.2934783### indicator species analysis
library(indicspecies)
## indicator species analysis
indval <- multipatt(community[,13:53], community$Microsite, control=how(nperm=999))
summary(indval)##
## Multilevel pattern analysis
## ---------------------------
##
## Association function: IndVal.g
## Significance level (alpha): 0.05
##
## Total number of species: 41
## Selected number of species: 15
## Number of species associated to 1 group: 15
##
## List of species associated to each combination:
##
## Group open #sps. 8
## stat p.value
## E.cicutarium 0.558 0.001 ***
## L.gracilis 0.427 0.001 ***
## A.wrangelianus 0.201 0.001 ***
## C.brevipes 0.171 0.033 *
## A.lentiginosus 0.149 0.024 *
## C.rigida 0.149 0.018 *
## A.grandiflora 0.129 0.016 *
## Boraginaceae.sp. 0.120 0.032 *
##
## Group shrub #sps. 7
## stat p.value
## C.fremontii 0.379 0.001 ***
## M.glabrata 0.277 0.001 ***
## P.tanacetifolia 0.267 0.001 ***
## A.tessellata 0.199 0.004 **
## B.nigra 0.179 0.021 *
## E.nitidum 0.172 0.007 **
## E.glyptosperma 0.128 0.043 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## species accumilation curves
accum1 <- specaccum(subset(community, Microsite=="shrub", select=13:53), method="random", permutations=1000)
accum2 <- specaccum(subset(community, Microsite=="open", select=13:53), method="random", permutations=1000)
plot(accum2, ci.col="Grey20", xlab="Sampling effort", cex.axis=1.5, cex.lab=1.8, ylab="Species richness")
plot(accum1,add=T , col="Grey50", ci.col="Grey60")
cluster analysis
library("cluster")
library("factoextra")
library("magrittr")
comm.sum <- community %>% group_by(Microsite, Site) %>% summarize_if(is.numeric, funs(sum))
comm.sum <- data.frame(comm.sum)
rownames(comm.sum) <- paste(comm.sum$Microsite,comm.sum$Site)
## coreelation matrix
### Distance measures
res.dist <- get_dist(comm.sum[,13:53], stand = TRUE, method = "euclidean")
fviz_dist(res.dist, gradient = list(low = "#00AFBB", mid = "white", high = "#FC4E07"))
## dendrogram
## transform data
comm.trans <- decostand(comm.sum[,13:53], "hell")
## clean data for ordination
## see distribution of spp
boxplot(comm.trans, xaxt="n")
labs <- colnames(comm.trans)
text(cex=0.8, x=1:41-1, y=-0.12, labs, xpd=TRUE, srt=45)
## remove spp with only one instancve
comm.trans <- comm.trans[,!colSums(comm.trans)==apply(comm.trans, 2, max)]
## replace outliers with mean
avg.max <- function(x) {
y = max(x)
avg = mean(x)
x[x==y] <- avg
return(x)
}
## dot chart to find outliers (remove or Avg)
#dotchart(comm.trans$E.cicutarium) ## Erodium
comm.trans[,"E.cicutarium"] <- avg.max(comm.trans$E.cicutarium) ## removed outlier
#dotchart(comm.trans$A.wrangelianus) ## Chilean trefoil
comm.trans[,"A.wrangelianus"] <- NULL ## removed entire species
#dotchart(comm.trans$A.lentiginosus) ##
comm.trans[,"A.lentiginosus"] <- avg.max(comm.trans$A.lentiginosus) ## removed outlier
#dotchart(comm.trans$H.vulgare) ## Hordeum
comm.trans[,"H.vulgare"] <- NULL ## removed entire species
#dotchart(comm.trans$Pectocarya.spp) ##
comm.trans[,"Pectocarya.spp"] <- avg.max(comm.trans$Pectocarya.spp) ## removed outlier
#dotchart(comm.trans$L.arizonicus) ## Lupin
comm.trans[,"L.arizonicus"] <- NULL ## removed entire species
#dotchart(comm.trans$M.bellioides) ##
comm.trans[,"M.bellioides"] <- NULL ## removed entire species
#dotchart(comm.trans$B.nigra) ## Mustard
comm.trans[,"B.nigra"] <- NULL ## removed entire species
#dotchart(comm.trans$N.demissum) ## Sand Matt
comm.trans[,"N.demissum"] <- avg.max(comm.trans$N.demissum) ## removed outlier
#dotchart(comm.trans$L.gracilis) ##
comm.trans[,"L.gracilis"] <- avg.max(comm.trans$L.gracilis) ## removed outlier
#dotchart(comm.trans$A.grandiflora) ## Amsinckia
comm.trans[,"A.grandiflora"] <- NULL ## removed entire species
#dotchart(comm.trans$B.diandrus) ## Bromus diandrus
comm.trans[,"B.diandrus"] <- NULL ## removed entire species
#dotchart(comm.trans$M.affinis) ##
comm.trans[,"M.affinis"] <- NULL ## removed entire species
#dotchart(comm.trans$P.crenulata) ##
comm.trans[,"P.crenulata"] <- NULL ## removed entire species
#dotchart(comm.trans$Erinoginum.spp) # buckwheat
comm.trans[,"Erinoginum.spp"] <- avg.max(comm.trans$Erinoginum.spp) ## removed outlier
#dotchart(comm.trans$C.lasiophyllus) #
comm.trans[,"C.lasiophyllus"] <- NULL ## removed entire species
library(usdm)
library(corrplot)
## Check for collinearity
vifcor(comm.trans)## 4 variables from the 25 input variables have collinearity problem:
##
## E.wallacei P.insularis E.glyptosperma L.decorus
##
## After excluding the collinear variables, the linear correlation coefficients ranges between:
## min correlation ( L.gracilis ~ S.barbatus ): -0.003266766
## max correlation ( C.fremontii ~ M.glabrata ): 0.8450794
##
## ---------- VIFs of the remained variables --------
## Variables VIF
## 1 B.rubens Inf
## 2 E.cicutarium Inf
## 3 S.barbatus Inf
## 4 C.barbigera Inf
## 5 C.claviformis Inf
## 6 A.lentiginosus Inf
## 7 C.rigida Inf
## 8 P.tanacetifolia Inf
## 9 Pectocarya.spp Inf
## 10 D.capitatum Inf
## 11 C.brevipes Inf
## 12 M.glabrata Inf
## 13 C.fremontii Inf
## 14 C.intermedia Inf
## 15 N.demissum Inf
## 16 L.gracilis Inf
## 17 A.tessellata Inf
## 18 P.secunda Inf
## 19 L.sparsiflorus Inf
## 20 Erinoginum.spp Inf
## 21 E.nitidum Inf## drop Claviformis because of collinear problems in P.insularis
comm.trans[,"C.claviformis"] <- NULL ## removed entire species
comm.trans[,"L.decorus"] <- NULL ## removed entire species
comm.trans[,"E.glyptosperma"] <- NULL ## removed entire species
comm.trans[,"E.wallacei"] <- NULL ## removed entire species
cor.dat <- cor(comm.trans[,1:20])
corrplot(cor.dat, method="number")
## calculate distances
dis <- vegdist(comm.trans, method="bray")
## cluster analysis
clus <- hclust(dis, "ward.D2")
## cluster colours
colz <- c("#3e4444", "#86af49", "#405d27","#c1946a")
fviz_dend(clus, k = 4, # Cut in four groups
cex = 0.5, # label size
k_colors = colz,
color_labels_by_k = TRUE, # color labels by groups
rect = TRUE # Add rectangle around groups
)
grp <- cutree(clus, 4)
## Interpretation of classes
## get soil moisture from site/microsite
smc.avg <- smc.end %>% group_by(Site, Microsite) %>% summarize(mean.smc=mean(swc.avg, na.rm=T))
smc.avg <- data.frame(smc.avg)
smc.avg[,"grp"] <- as.factor(c(3,3,2,1,4,4,1,1,4,4,3,3,2,2))
boxplot(mean.smc ~ grp, data=smc.avg)
## conduct ordination
ord <- rda(comm.trans)
(R2adj <- RsquareAdj(rda1)$adj.r.squared) ## 50.4% of variation explained## [1] 0.3871872## calculate priority
spp.priority <- colSums(comm.trans)
## plot ordination
par(mar=c(4.5,4.5,0.5,0.5))
plot(ord, type="n")
ordiellipse(ord, grp, lty = 2, col = colz, draw="polygon", alpha=150)
orditorp(ord, display = "species", cex = 0.7, col = "darkred", priority=spp.priority, air=0.5)
orditorp(ord, display = "sites", cex = 0.7, col = "black")
##collect environmental variables for site
nutrients <- read.csv("Data/ERG.soilnutrients.csv")
nutrients.mean <- nutrients %>% group_by(microsite, Site) %>% summarise_all(funs(mean))
nutrients.vars <- data.frame(nutrients.mean)
## summarize daily means across sites
means <- HOBO.data %>% group_by(Microsite, Site) %>% summarize(Temp.=mean(Temp, na.rm=T),rh=mean(RH, na.rm=T),temp.se=se(Temp),rh.se=se(RH))
means <- data.frame(means)
## soil moisture
smc.avg <- smc.early %>% group_by(Microsite, Site) %>% summarize(SMC=mean(swc.avg, na.rm=T))
envs <- data.frame(swc=smc.avg[,"SMC"],nutrients.vars[,c("N","P","K")], means[,c("Temp.","rh")])
## Check for collinearity
cor(envs)## SMC N P K Temp. rh
## SMC 1.0000000 -0.31692385 0.44854712 0.81694831 -0.6306962 0.9176776
## N -0.3169238 1.00000000 0.05879919 -0.05275422 0.4188434 -0.4374109
## P 0.4485471 0.05879919 1.00000000 0.64885814 -0.5264257 0.5353719
## K 0.8169483 -0.05275422 0.64885814 1.00000000 -0.5705341 0.7740852
## Temp. -0.6306962 0.41884336 -0.52642569 -0.57053407 1.0000000 -0.8625519
## rh 0.9176776 -0.43741089 0.53537193 0.77408521 -0.8625519 1.0000000envs[,"K"] <- NULL ## drop potassium for being correlated
envs[,"rh"] <- NULL ## drop humidity for being correlated
ord.env <- envfit(ord, envs)
plot(ord.env)
# ordicluster(ord, clus, col="blue")Basic community data
community## Year ID Site Rep Lat Long Elevation Gradient
## 1 2017 1 PanocheHills 1 36.70001 -120.8011 656.3209 1
## 2 2017 1 PanocheHills 1 36.70001 -120.8011 656.3209 1
## 3 2017 2 PanocheHills 2 36.70005 -120.8012 656.1902 1
## 4 2017 2 PanocheHills 2 36.70005 -120.8012 656.1902 1
## 5 2017 3 PanocheHills 3 36.70010 -120.8015 656.6475 1
## 6 2017 3 PanocheHills 3 36.70010 -120.8015 656.6475 1
## 7 2017 4 PanocheHills 4 36.70012 -120.8014 657.3752 1
## 8 2017 4 PanocheHills 4 36.70012 -120.8014 657.3752 1
## 9 2017 5 PanocheHills 5 36.70019 -120.8016 656.5075 1
## 10 2017 5 PanocheHills 5 36.70019 -120.8016 656.5075 1
## 11 2017 6 PanocheHills 6 36.70026 -120.8014 655.5837 1
## 12 2017 6 PanocheHills 6 36.70026 -120.8014 655.5837 1
## 13 2017 7 PanocheHills 7 36.70037 -120.8014 655.4344 1
## 14 2017 7 PanocheHills 7 36.70037 -120.8014 655.4344 1
## 15 2017 8 PanocheHills 8 36.70045 -120.8015 655.3132 1
## 16 2017 8 PanocheHills 8 36.70045 -120.8015 655.3132 1
## 17 2017 9 PanocheHills 9 36.70052 -120.8015 655.0519 1
## 18 2017 9 PanocheHills 9 36.70052 -120.8015 655.0519 1
## 19 2017 10 PanocheHills 10 36.70044 -120.8016 655.5371 1
## 20 2017 10 PanocheHills 10 36.70044 -120.8016 655.5371 1
## 21 2017 11 PanocheHills 11 36.70057 -120.8017 656.4329 1
## 22 2017 11 PanocheHills 11 36.70057 -120.8017 656.4329 1
## 23 2017 12 PanocheHills 12 36.70063 -120.8019 657.0300 1
## 24 2017 12 PanocheHills 12 36.70063 -120.8019 657.0300 1
## 25 2017 13 PanocheHills 13 36.70060 -120.8020 657.1606 1
## 26 2017 13 PanocheHills 13 36.70060 -120.8020 657.1606 1
## 27 2017 14 PanocheHills 14 36.70064 -120.8017 656.7687 1
## 28 2017 14 PanocheHills 14 36.70064 -120.8017 656.7687 1
## 29 2017 15 PanocheHills 15 36.70064 -120.8022 657.2166 1
## 30 2017 15 PanocheHills 15 36.70064 -120.8022 657.2166 1
## 31 2017 16 PanocheHills 16 36.70074 -120.8022 656.9554 1
## 32 2017 16 PanocheHills 16 36.70074 -120.8022 656.9554 1
## 33 2017 17 PanocheHills 17 36.70086 -120.8022 656.0223 1
## 34 2017 17 PanocheHills 17 36.70086 -120.8022 656.0223 1
## 35 2017 18 PanocheHills 18 36.70080 -120.8023 656.5541 1
## 36 2017 18 PanocheHills 18 36.70080 -120.8023 656.5541 1
## 37 2017 19 PanocheHills 19 36.70073 -120.8024 656.1996 1
## 38 2017 19 PanocheHills 19 36.70073 -120.8024 656.1996 1
## 39 2017 20 PanocheHills 20 36.70069 -120.8023 655.4438 1
## 40 2017 20 PanocheHills 20 36.70069 -120.8023 655.4438 1
## 41 2017 21 PanocheHills 21 36.70053 -120.8023 655.8543 1
## 42 2017 21 PanocheHills 21 36.70053 -120.8023 655.8543 1
## 43 2017 22 PanocheHills 22 36.70048 -120.8021 657.1420 1
## 44 2017 22 PanocheHills 22 36.70048 -120.8021 657.1420 1
## 45 2017 23 PanocheHills 23 36.70042 -120.8021 657.7485 1
## 46 2017 23 PanocheHills 23 36.70042 -120.8021 657.7485 1
## 47 2017 24 PanocheHills 24 36.70036 -120.8020 658.2523 1
## 48 2017 24 PanocheHills 24 36.70036 -120.8020 658.2523 1
## 49 2017 25 PanocheHills 25 36.70035 -120.8019 658.7189 1
## 50 2017 25 PanocheHills 25 36.70035 -120.8019 658.7189 1
## 51 2017 26 PanocheHills 26 36.70026 -120.8020 658.9055 1
## 52 2017 26 PanocheHills 26 36.70026 -120.8020 658.9055 1
## 53 2017 27 PanocheHills 27 36.70013 -120.8020 658.9988 1
## 54 2017 27 PanocheHills 27 36.70013 -120.8020 658.9988 1
## 55 2017 28 PanocheHills 28 36.70015 -120.8020 659.0361 1
## 56 2017 28 PanocheHills 28 36.70015 -120.8020 659.0361 1
## 57 2017 29 PanocheHills 29 36.70009 -120.8019 658.9614 1
## 58 2017 29 PanocheHills 29 36.70009 -120.8019 658.9614 1
## 59 2017 30 PanocheHills 30 36.70005 -120.8019 659.1201 1
## 60 2017 30 PanocheHills 30 36.70005 -120.8019 659.1201 1
## 61 2017 31 Cuyama 1 34.85522 -119.4885 806.8343 2
## 62 2017 31 Cuyama 1 34.85522 -119.4885 806.8343 2
## 63 2017 32 Cuyama 2 34.85518 -119.4886 806.5264 2
## 64 2017 32 Cuyama 2 34.85518 -119.4886 806.5264 2
## 65 2017 33 Cuyama 3 34.85512 -119.4886 806.4984 2
## 66 2017 33 Cuyama 3 34.85512 -119.4886 806.4984 2
## 67 2017 34 Cuyama 4 34.85506 -119.4885 806.6196 2
## 68 2017 34 Cuyama 4 34.85506 -119.4885 806.6196 2
## 69 2017 35 Cuyama 5 34.85505 -119.4884 807.0115 2
## 70 2017 35 Cuyama 5 34.85505 -119.4884 807.0115 2
## 71 2017 36 Cuyama 6 34.85500 -119.4884 806.0505 2
## 72 2017 36 Cuyama 6 34.85500 -119.4884 806.0505 2
## 73 2017 37 Cuyama 7 34.85489 -119.4885 808.0753 2
## 74 2017 37 Cuyama 7 34.85489 -119.4885 808.0753 2
## 75 2017 38 Cuyama 8 34.85488 -119.4884 808.7097 2
## 76 2017 38 Cuyama 8 34.85488 -119.4884 808.7097 2
## 77 2017 39 Cuyama 9 34.85489 -119.4883 808.5231 2
## 78 2017 39 Cuyama 9 34.85489 -119.4883 808.5231 2
## 79 2017 40 Cuyama 10 34.85476 -119.4884 809.9227 2
## 80 2017 40 Cuyama 10 34.85476 -119.4884 809.9227 2
## 81 2017 41 Cuyama 11 34.85462 -119.4885 809.3256 2
## 82 2017 41 Cuyama 11 34.85462 -119.4885 809.3256 2
## 83 2017 42 Cuyama 12 34.85465 -119.4884 809.5961 2
## 84 2017 42 Cuyama 12 34.85465 -119.4884 809.5961 2
## 85 2017 43 Cuyama 13 34.85454 -119.4884 810.1840 2
## 86 2017 43 Cuyama 13 34.85454 -119.4884 810.1840 2
## 87 2017 44 Cuyama 14 34.85442 -119.4884 809.8107 2
## 88 2017 44 Cuyama 14 34.85442 -119.4884 809.8107 2
## 89 2017 45 Cuyama 15 34.85441 -119.4882 810.0720 2
## 90 2017 45 Cuyama 15 34.85441 -119.4882 810.0720 2
## 91 2017 46 Cuyama 16 34.85436 -119.4881 810.3986 2
## 92 2017 46 Cuyama 16 34.85436 -119.4881 810.3986 2
## 93 2017 47 Cuyama 17 34.85443 -119.4880 810.0161 2
## 94 2017 47 Cuyama 17 34.85443 -119.4880 810.0161 2
## 95 2017 48 Cuyama 18 34.85449 -119.4880 811.1917 2
## 96 2017 48 Cuyama 18 34.85449 -119.4880 811.1917 2
## 97 2017 49 Cuyama 19 34.85461 -119.4879 811.6675 2
## 98 2017 49 Cuyama 19 34.85461 -119.4879 811.6675 2
## 99 2017 50 Cuyama 20 34.85472 -119.4878 809.8854 2
## 100 2017 50 Cuyama 20 34.85472 -119.4878 809.8854 2
## 101 2017 51 Cuyama 21 34.85472 -119.4877 810.4172 2
## 102 2017 51 Cuyama 21 34.85472 -119.4877 810.4172 2
## 103 2017 52 Cuyama 22 34.85484 -119.4876 811.3410 2
## 104 2017 52 Cuyama 22 34.85484 -119.4876 811.3410 2
## 105 2017 53 Cuyama 23 34.85492 -119.4874 810.9584 2
## 106 2017 53 Cuyama 23 34.85492 -119.4874 810.9584 2
## 107 2017 54 Cuyama 24 34.85496 -119.4873 811.6302 2
## 108 2017 54 Cuyama 24 34.85496 -119.4873 811.6302 2
## 109 2017 55 Cuyama 25 34.85500 -119.4874 811.9008 2
## 110 2017 55 Cuyama 25 34.85500 -119.4874 811.9008 2
## 111 2017 56 Cuyama 26 34.85510 -119.4875 812.1807 2
## 112 2017 56 Cuyama 26 34.85510 -119.4875 812.1807 2
## 113 2017 57 Cuyama 27 34.85511 -119.4876 812.1248 2
## 114 2017 57 Cuyama 27 34.85511 -119.4876 812.1248 2
## 115 2017 58 Cuyama 28 34.85508 -119.4876 812.6193 2
## 116 2017 58 Cuyama 28 34.85508 -119.4876 812.6193 2
## 117 2017 59 Cuyama 29 34.85499 -119.4879 811.4623 2
## 118 2017 59 Cuyama 29 34.85499 -119.4879 811.4623 2
## 119 2017 60 Cuyama 30 34.85502 -119.4881 811.7795 2
## 120 2017 60 Cuyama 30 34.85502 -119.4881 811.7795 2
## 121 2017 61 Barstow 1 35.09405 -116.8349 496.0200 4
## 122 2017 61 Barstow 1 35.09405 -116.8349 496.0200 4
## 123 2017 62 Barstow 2 35.09391 -116.8348 496.1690 4
## 124 2017 62 Barstow 2 35.09391 -116.8348 496.1690 4
## 125 2017 63 Barstow 3 35.09381 -116.8349 495.4130 4
## 126 2017 63 Barstow 3 35.09381 -116.8349 495.4130 4
## 127 2017 64 Barstow 4 35.09373 -116.8350 495.4600 4
## 128 2017 64 Barstow 4 35.09373 -116.8350 495.4600 4
## 129 2017 65 Barstow 5 35.09372 -116.8350 494.9840 4
## 130 2017 65 Barstow 5 35.09372 -116.8350 494.9840 4
## 131 2017 66 Barstow 6 35.09361 -116.8350 494.7600 4
## 132 2017 66 Barstow 6 35.09361 -116.8350 494.7600 4
## 133 2017 67 Barstow 7 35.09359 -116.8351 495.0870 4
## 134 2017 67 Barstow 7 35.09359 -116.8351 495.0870 4
## 135 2017 68 Barstow 8 35.09358 -116.8351 495.1050 4
## 136 2017 68 Barstow 8 35.09358 -116.8351 495.1050 4
## 137 2017 69 Barstow 9 35.09353 -116.8351 495.1520 4
## 138 2017 69 Barstow 9 35.09353 -116.8351 495.1520 4
## 139 2017 70 Barstow 10 35.09342 -116.8350 494.9280 4
## 140 2017 70 Barstow 10 35.09342 -116.8350 494.9280 4
## 141 2017 71 Barstow 11 35.09338 -116.8352 494.8630 4
## 142 2017 71 Barstow 11 35.09338 -116.8352 494.8630 4
## 143 2017 72 Barstow 12 35.09336 -116.8351 494.9000 4
## 144 2017 72 Barstow 12 35.09336 -116.8351 494.9000 4
## 145 2017 73 Barstow 13 35.09332 -116.8351 494.8910 4
## 146 2017 73 Barstow 13 35.09332 -116.8351 494.8910 4
## 147 2017 74 Barstow 14 35.09325 -116.8352 494.5830 4
## 148 2017 74 Barstow 14 35.09325 -116.8352 494.5830 4
## 149 2017 75 Barstow 15 35.09318 -116.8352 495.0680 4
## 150 2017 75 Barstow 15 35.09318 -116.8352 495.0680 4
## 151 2017 76 Barstow 16 35.09313 -116.8352 494.8810 4
## 152 2017 76 Barstow 16 35.09313 -116.8352 494.8810 4
## 153 2017 77 Barstow 17 35.09310 -116.8353 494.6010 4
## 154 2017 77 Barstow 17 35.09310 -116.8353 494.6010 4
## 155 2017 78 Barstow 18 35.09299 -116.8352 494.1160 4
## 156 2017 78 Barstow 18 35.09299 -116.8352 494.1160 4
## 157 2017 79 Barstow 19 35.09289 -116.8352 493.9670 4
## 158 2017 79 Barstow 19 35.09289 -116.8352 493.9670 4
## 159 2017 80 Barstow 20 35.09276 -116.8352 493.7340 4
## 160 2017 80 Barstow 20 35.09276 -116.8352 493.7340 4
## 161 2017 81 Barstow 21 35.09275 -116.8351 573.0444 4
## 162 2017 81 Barstow 21 35.09275 -116.8351 573.0444 4
## 163 2017 82 Barstow 22 35.09271 -116.8350 572.1393 4
## 164 2017 82 Barstow 22 35.09271 -116.8350 572.1393 4
## 165 2017 83 Barstow 23 35.09280 -116.8349 572.9885 4
## 166 2017 83 Barstow 23 35.09280 -116.8349 572.9885 4
## 167 2017 84 Barstow 24 35.09275 -116.8348 572.3166 4
## 168 2017 84 Barstow 24 35.09275 -116.8348 572.3166 4
## 169 2017 85 Barstow 25 35.09280 -116.8347 571.7568 4
## 170 2017 85 Barstow 25 35.09280 -116.8347 571.7568 4
## 171 2017 86 Barstow 26 35.09287 -116.8348 571.9061 4
## 172 2017 86 Barstow 26 35.09287 -116.8348 571.9061 4
## 173 2017 87 Barstow 27 35.09290 -116.8346 571.6915 4
## 174 2017 87 Barstow 27 35.09290 -116.8346 571.6915 4
## 175 2017 88 Barstow 28 35.09295 -116.8345 571.9341 4
## 176 2017 88 Barstow 28 35.09295 -116.8345 571.9341 4
## 177 2017 89 Barstow 29 35.09302 -116.8347 571.4862 4
## 178 2017 89 Barstow 29 35.09302 -116.8347 571.4862 4
## 179 2017 90 Barstow 30 35.09311 -116.8347 571.7195 4
## 180 2017 90 Barstow 30 35.09311 -116.8347 571.7195 4
## 181 2017 91 HeartofMojave 1 34.69820 -115.6842 784.7300 5
## 182 2017 91 HeartofMojave 1 34.69820 -115.6842 784.7300 5
## 183 2017 92 HeartofMojave 2 34.69811 -115.6841 784.6460 5
## 184 2017 92 HeartofMojave 2 34.69811 -115.6841 784.6460 5
## 185 2017 93 HeartofMojave 3 34.69805 -115.6841 784.8510 5
## 186 2017 93 HeartofMojave 3 34.69805 -115.6841 784.8510 5
## 187 2017 94 HeartofMojave 4 34.69798 -115.6841 784.8330 5
## 188 2017 94 HeartofMojave 4 34.69798 -115.6841 784.8330 5
## 189 2017 95 HeartofMojave 5 34.69792 -115.6841 784.5530 5
## 190 2017 95 HeartofMojave 5 34.69792 -115.6841 784.5530 5
## 191 2017 96 HeartofMojave 6 34.69794 -115.6843 784.2630 5
## 192 2017 96 HeartofMojave 6 34.69794 -115.6843 784.2630 5
## 193 2017 97 HeartofMojave 7 34.69789 -115.6843 784.3660 5
## 194 2017 97 HeartofMojave 7 34.69789 -115.6843 784.3660 5
## 195 2017 98 HeartofMojave 8 34.69784 -115.6844 784.2350 5
## 196 2017 98 HeartofMojave 8 34.69784 -115.6844 784.2350 5
## 197 2017 99 HeartofMojave 9 34.69782 -115.6843 783.9930 5
## 198 2017 99 HeartofMojave 9 34.69782 -115.6843 783.9930 5
## 199 2017 100 HeartofMojave 10 34.69779 -115.6844 783.2460 5
## 200 2017 100 HeartofMojave 10 34.69779 -115.6844 783.2460 5
## 201 2017 101 HeartofMojave 11 34.69776 -115.6843 783.0600 5
## 202 2017 101 HeartofMojave 11 34.69776 -115.6843 783.0600 5
## 203 2017 102 HeartofMojave 12 34.69772 -115.6844 783.0410 5
## 204 2017 102 HeartofMojave 12 34.69772 -115.6844 783.0410 5
## 205 2017 103 HeartofMojave 13 34.69762 -115.6844 783.1720 5
## 206 2017 103 HeartofMojave 13 34.69762 -115.6844 783.1720 5
## 207 2017 104 HeartofMojave 14 34.69764 -115.6845 782.6680 5
## 208 2017 104 HeartofMojave 14 34.69764 -115.6845 782.6680 5
## 209 2017 105 HeartofMojave 15 34.69756 -115.6844 782.5840 5
## 210 2017 105 HeartofMojave 15 34.69756 -115.6844 782.5840 5
## 211 2017 106 HeartofMojave 16 34.69747 -115.6843 781.8750 5
## 212 2017 106 HeartofMojave 16 34.69747 -115.6843 781.8750 5
## 213 2017 107 HeartofMojave 17 34.69735 -115.6843 783.0130 5
## 214 2017 107 HeartofMojave 17 34.69735 -115.6843 783.0130 5
## 215 2017 108 HeartofMojave 18 34.69730 -115.6843 782.3690 5
## 216 2017 108 HeartofMojave 18 34.69730 -115.6843 782.3690 5
## 217 2017 109 HeartofMojave 19 34.69718 -115.6843 781.3430 5
## 218 2017 109 HeartofMojave 19 34.69718 -115.6843 781.3430 5
## 219 2017 110 HeartofMojave 20 34.69714 -115.6843 781.0820 5
## 220 2017 110 HeartofMojave 20 34.69714 -115.6843 781.0820 5
## 221 2017 111 HeartofMojave 21 34.69721 -115.6842 770.7805 5
## 222 2017 111 HeartofMojave 21 34.69721 -115.6842 770.7805 5
## 223 2017 112 HeartofMojave 22 34.69729 -115.6841 770.9671 5
## 224 2017 112 HeartofMojave 22 34.69729 -115.6841 770.9671 5
## 225 2017 113 HeartofMojave 23 34.69745 -115.6838 773.9343 5
## 226 2017 113 HeartofMojave 23 34.69745 -115.6838 773.9343 5
## 227 2017 114 HeartofMojave 24 34.69748 -115.6838 773.0945 5
## 228 2017 114 HeartofMojave 24 34.69748 -115.6838 773.0945 5
## 229 2017 115 HeartofMojave 25 34.69748 -115.6837 773.9996 5
## 230 2017 115 HeartofMojave 25 34.69748 -115.6837 773.9996 5
## 231 2017 116 HeartofMojave 26 34.69755 -115.6837 774.5034 5
## 232 2017 116 HeartofMojave 26 34.69755 -115.6837 774.5034 5
## 233 2017 117 HeartofMojave 27 34.69761 -115.6836 774.8766 5
## 234 2017 117 HeartofMojave 27 34.69761 -115.6836 774.8766 5
## 235 2017 118 HeartofMojave 28 34.69764 -115.6836 775.0259 5
## 236 2017 118 HeartofMojave 28 34.69764 -115.6836 775.0259 5
## 237 2017 119 HeartofMojave 29 34.69769 -115.6837 774.9606 5
## 238 2017 119 HeartofMojave 29 34.69769 -115.6837 774.9606 5
## 239 2017 120 HeartofMojave 30 34.69772 -115.6837 775.0726 5
## 240 2017 120 HeartofMojave 30 34.69772 -115.6837 775.0726 5
## 241 2017 121 SheepholeValley 1 34.20568 -115.7197 545.9200 6
## 242 2017 121 SheepholeValley 1 34.20568 -115.7197 545.9200 6
## 243 2017 122 SheepholeValley 2 34.20574 -115.7195 546.5730 6
## 244 2017 122 SheepholeValley 2 34.20574 -115.7195 546.5730 6
## 245 2017 123 SheepholeValley 3 34.20565 -115.7194 546.6290 6
## 246 2017 123 SheepholeValley 3 34.20565 -115.7194 546.6290 6
## 247 2017 124 SheepholeValley 4 34.20559 -115.7192 546.2470 6
## 248 2017 124 SheepholeValley 4 34.20559 -115.7192 546.2470 6
## 249 2017 125 SheepholeValley 5 34.20563 -115.7192 546.5170 6
## 250 2017 125 SheepholeValley 5 34.20563 -115.7192 546.5170 6
## 251 2017 126 SheepholeValley 6 34.20558 -115.7190 546.2930 6
## 252 2017 126 SheepholeValley 6 34.20558 -115.7190 546.2930 6
## 253 2017 127 SheepholeValley 7 34.20561 -115.7190 545.8920 6
## 254 2017 127 SheepholeValley 7 34.20561 -115.7190 545.8920 6
## 255 2017 128 SheepholeValley 8 34.20555 -115.7190 545.7430 6
## 256 2017 128 SheepholeValley 8 34.20555 -115.7190 545.7430 6
## 257 2017 129 SheepholeValley 9 34.20572 -115.7188 546.5080 6
## 258 2017 129 SheepholeValley 9 34.20572 -115.7188 546.5080 6
## 259 2017 130 SheepholeValley 10 34.20577 -115.7187 546.9090 6
## 260 2017 130 SheepholeValley 10 34.20577 -115.7187 546.9090 6
## 261 2017 131 SheepholeValley 11 34.20580 -115.7186 547.1330 6
## 262 2017 131 SheepholeValley 11 34.20580 -115.7186 547.1330 6
## 263 2017 132 SheepholeValley 12 34.20594 -115.7186 547.8890 6
## 264 2017 132 SheepholeValley 12 34.20594 -115.7186 547.8890 6
## 265 2017 133 SheepholeValley 13 34.20611 -115.7187 548.4580 6
## 266 2017 133 SheepholeValley 13 34.20611 -115.7187 548.4580 6
## 267 2017 134 SheepholeValley 14 34.20626 -115.7190 549.5310 6
## 268 2017 134 SheepholeValley 14 34.20626 -115.7190 549.5310 6
## 269 2017 135 SheepholeValley 15 34.20632 -115.7192 550.0070 6
## 270 2017 135 SheepholeValley 15 34.20632 -115.7192 550.0070 6
## 271 2017 136 SheepholeValley 16 34.20631 -115.7194 550.3800 6
## 272 2017 136 SheepholeValley 16 34.20631 -115.7194 550.3800 6
## 273 2017 137 SheepholeValley 17 34.20628 -115.7196 549.9420 6
## 274 2017 137 SheepholeValley 17 34.20628 -115.7196 549.9420 6
## 275 2017 138 SheepholeValley 18 34.20625 -115.7196 550.1560 6
## 276 2017 138 SheepholeValley 18 34.20625 -115.7196 550.1560 6
## 277 2017 139 SheepholeValley 19 34.20627 -115.7197 549.4560 6
## 278 2017 139 SheepholeValley 19 34.20627 -115.7197 549.4560 6
## 279 2017 140 SheepholeValley 20 34.20618 -115.7198 549.1950 6
## 280 2017 140 SheepholeValley 20 34.20618 -115.7198 549.1950 6
## 281 2017 141 SheepholeValley 21 34.20650 -115.7194 598.3586 6
## 282 2017 141 SheepholeValley 21 34.20650 -115.7194 598.3586 6
## 283 2017 142 SheepholeValley 22 34.20652 -115.7192 597.8641 6
## 284 2017 142 SheepholeValley 22 34.20652 -115.7192 597.8641 6
## 285 2017 143 SheepholeValley 23 34.20664 -115.7191 599.4130 6
## 286 2017 143 SheepholeValley 23 34.20664 -115.7191 599.4130 6
## 287 2017 144 SheepholeValley 24 34.20678 -115.7192 600.1594 6
## 288 2017 144 SheepholeValley 24 34.20678 -115.7192 600.1594 6
## 289 2017 145 SheepholeValley 25 34.20664 -115.7192 597.9014 6
## 290 2017 145 SheepholeValley 25 34.20664 -115.7192 597.9014 6
## 291 2017 146 SheepholeValley 26 34.20659 -115.7193 597.4069 6
## 292 2017 146 SheepholeValley 26 34.20659 -115.7193 597.4069 6
## 293 2017 147 SheepholeValley 27 34.20659 -115.7195 595.8207 6
## 294 2017 147 SheepholeValley 27 34.20659 -115.7195 595.8207 6
## 295 2017 148 SheepholeValley 28 34.20658 -115.7198 595.4101 6
## 296 2017 148 SheepholeValley 28 34.20658 -115.7198 595.4101 6
## 297 2017 149 SheepholeValley 29 34.20655 -115.7199 595.5687 6
## 298 2017 149 SheepholeValley 29 34.20655 -115.7199 595.5687 6
## 299 2017 150 SheepholeValley 30 34.20661 -115.7199 596.3432 6
## 300 2017 150 SheepholeValley 30 34.20661 -115.7199 596.3432 6
## 301 2017 151 Tecopa 1 35.85152 -116.1867 699.4567 7
## 302 2017 151 Tecopa 1 35.85152 -116.1867 699.4567 7
## 303 2017 152 Tecopa 2 35.85145 -116.1867 699.5779 7
## 304 2017 152 Tecopa 2 35.85145 -116.1867 699.5779 7
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## 626 2016 103 HeartofMojave 13 34.69762 -115.6844 783.1720 5
## 627 2016 104 HeartofMojave 14 34.69764 -115.6845 782.6680 5
## 628 2016 104 HeartofMojave 14 34.69764 -115.6845 782.6680 5
## 629 2016 105 HeartofMojave 15 34.69756 -115.6844 782.5840 5
## 630 2016 105 HeartofMojave 15 34.69756 -115.6844 782.5840 5
## 631 2016 106 HeartofMojave 16 34.69747 -115.6843 781.8750 5
## 632 2016 106 HeartofMojave 16 34.69747 -115.6843 781.8750 5
## 633 2016 107 HeartofMojave 17 34.69735 -115.6843 783.0130 5
## 634 2016 107 HeartofMojave 17 34.69735 -115.6843 783.0130 5
## 635 2016 108 HeartofMojave 18 34.69730 -115.6843 782.3690 5
## 636 2016 108 HeartofMojave 18 34.69730 -115.6843 782.3690 5
## 637 2016 109 HeartofMojave 19 34.69718 -115.6843 781.3430 5
## 638 2016 109 HeartofMojave 19 34.69718 -115.6843 781.3430 5
## 639 2016 110 HeartofMojave 20 34.69714 -115.6843 781.0820 5
## 640 2016 110 HeartofMojave 20 34.69714 -115.6843 781.0820 5
## 641 2016 111 HeartofMojave 21 34.69721 -115.6842 770.7805 5
## 642 2016 111 HeartofMojave 21 34.69721 -115.6842 770.7805 5
## 643 2016 112 HeartofMojave 22 34.69729 -115.6841 770.9671 5
## 644 2016 112 HeartofMojave 22 34.69729 -115.6841 770.9671 5
## 645 2016 113 HeartofMojave 23 34.69745 -115.6838 773.9343 5
## 646 2016 113 HeartofMojave 23 34.69745 -115.6838 773.9343 5
## 647 2016 114 HeartofMojave 24 34.69748 -115.6838 773.0945 5
## 648 2016 114 HeartofMojave 24 34.69748 -115.6838 773.0945 5
## 649 2016 115 HeartofMojave 25 34.69748 -115.6837 773.9996 5
## 650 2016 115 HeartofMojave 25 34.69748 -115.6837 773.9996 5
## 651 2016 116 HeartofMojave 26 34.69755 -115.6837 774.5034 5
## 652 2016 116 HeartofMojave 26 34.69755 -115.6837 774.5034 5
## 653 2016 117 HeartofMojave 27 34.69761 -115.6836 774.8766 5
## 654 2016 117 HeartofMojave 27 34.69761 -115.6836 774.8766 5
## 655 2016 118 HeartofMojave 28 34.69764 -115.6836 775.0259 5
## 656 2016 118 HeartofMojave 28 34.69764 -115.6836 775.0259 5
## 657 2016 119 HeartofMojave 29 34.69769 -115.6837 774.9606 5
## 658 2016 119 HeartofMojave 29 34.69769 -115.6837 774.9606 5
## 659 2016 120 HeartofMojave 30 34.69772 -115.6837 775.0726 5
## 660 2016 120 HeartofMojave 30 34.69772 -115.6837 775.0726 5
## 661 2016 121 SheepholeValley 1 34.20568 -115.7197 545.9200 6
## 662 2016 121 SheepholeValley 1 34.20568 -115.7197 545.9200 6
## 663 2016 122 SheepholeValley 2 34.20574 -115.7195 546.5730 6
## 664 2016 122 SheepholeValley 2 34.20574 -115.7195 546.5730 6
## 665 2016 123 SheepholeValley 3 34.20565 -115.7194 546.6290 6
## 666 2016 123 SheepholeValley 3 34.20565 -115.7194 546.6290 6
## 667 2016 124 SheepholeValley 4 34.20559 -115.7192 546.2470 6
## 668 2016 124 SheepholeValley 4 34.20559 -115.7192 546.2470 6
## 669 2016 125 SheepholeValley 5 34.20563 -115.7192 546.5170 6
## 670 2016 125 SheepholeValley 5 34.20563 -115.7192 546.5170 6
## 671 2016 126 SheepholeValley 6 34.20558 -115.7190 546.2930 6
## 672 2016 126 SheepholeValley 6 34.20558 -115.7190 546.2930 6
## 673 2016 127 SheepholeValley 7 34.20561 -115.7190 545.8920 6
## 674 2016 127 SheepholeValley 7 34.20561 -115.7190 545.8920 6
## 675 2016 128 SheepholeValley 8 34.20555 -115.7190 545.7430 6
## 676 2016 128 SheepholeValley 8 34.20555 -115.7190 545.7430 6
## 677 2016 129 SheepholeValley 9 34.20572 -115.7188 546.5080 6
## 678 2016 129 SheepholeValley 9 34.20572 -115.7188 546.5080 6
## 679 2016 130 SheepholeValley 10 34.20577 -115.7187 546.9090 6
## 680 2016 130 SheepholeValley 10 34.20577 -115.7187 546.9090 6
## 681 2016 131 SheepholeValley 11 34.20580 -115.7186 547.1330 6
## 682 2016 131 SheepholeValley 11 34.20580 -115.7186 547.1330 6
## 683 2016 132 SheepholeValley 12 34.20594 -115.7186 547.8890 6
## 684 2016 132 SheepholeValley 12 34.20594 -115.7186 547.8890 6
## 685 2016 133 SheepholeValley 13 34.20611 -115.7187 548.4580 6
## 686 2016 133 SheepholeValley 13 34.20611 -115.7187 548.4580 6
## 687 2016 134 SheepholeValley 14 34.20626 -115.7190 549.5310 6
## 688 2016 134 SheepholeValley 14 34.20626 -115.7190 549.5310 6
## 689 2016 135 SheepholeValley 15 34.20632 -115.7192 550.0070 6
## 690 2016 135 SheepholeValley 15 34.20632 -115.7192 550.0070 6
## 691 2016 136 SheepholeValley 16 34.20631 -115.7194 550.3800 6
## 692 2016 136 SheepholeValley 16 34.20631 -115.7194 550.3800 6
## 693 2016 137 SheepholeValley 17 34.20628 -115.7196 549.9420 6
## 694 2016 137 SheepholeValley 17 34.20628 -115.7196 549.9420 6
## 695 2016 138 SheepholeValley 18 34.20625 -115.7196 550.1560 6
## 696 2016 138 SheepholeValley 18 34.20625 -115.7196 550.1560 6
## 697 2016 139 SheepholeValley 19 34.20627 -115.7197 549.4560 6
## 698 2016 139 SheepholeValley 19 34.20627 -115.7197 549.4560 6
## 699 2016 140 SheepholeValley 20 34.20618 -115.7198 549.1950 6
## 700 2016 140 SheepholeValley 20 34.20618 -115.7198 549.1950 6
## 701 2016 141 SheepholeValley 21 34.20650 -115.7194 598.3586 6
## 702 2016 141 SheepholeValley 21 34.20650 -115.7194 598.3586 6
## 703 2016 142 SheepholeValley 22 34.20652 -115.7192 597.8641 6
## 704 2016 142 SheepholeValley 22 34.20652 -115.7192 597.8641 6
## 705 2016 143 SheepholeValley 23 34.20664 -115.7191 599.4130 6
## 706 2016 143 SheepholeValley 23 34.20664 -115.7191 599.4130 6
## 707 2016 144 SheepholeValley 24 34.20678 -115.7192 600.1594 6
## 708 2016 144 SheepholeValley 24 34.20678 -115.7192 600.1594 6
## 709 2016 145 SheepholeValley 25 34.20664 -115.7192 597.9014 6
## 710 2016 145 SheepholeValley 25 34.20664 -115.7192 597.9014 6
## 711 2016 146 SheepholeValley 26 34.20659 -115.7193 597.4069 6
## 712 2016 146 SheepholeValley 26 34.20659 -115.7193 597.4069 6
## 713 2016 147 SheepholeValley 27 34.20659 -115.7195 595.8207 6
## 714 2016 147 SheepholeValley 27 34.20659 -115.7195 595.8207 6
## 715 2016 148 SheepholeValley 28 34.20658 -115.7198 595.4101 6
## 716 2016 148 SheepholeValley 28 34.20658 -115.7198 595.4101 6
## 717 2016 149 SheepholeValley 29 34.20655 -115.7199 595.5687 6
## 718 2016 149 SheepholeValley 29 34.20655 -115.7199 595.5687 6
## 719 2016 150 SheepholeValley 30 34.20661 -115.7199 596.3432 6
## 720 2016 150 SheepholeValley 30 34.20661 -115.7199 596.3432 6
## 721 2016 151 Tecopa 1 35.85152 -116.1867 699.4567 7
## 722 2016 151 Tecopa 1 35.85152 -116.1867 699.4567 7
## 723 2016 152 Tecopa 2 35.85145 -116.1867 699.5779 7
## 724 2016 152 Tecopa 2 35.85145 -116.1867 699.5779 7
## 725 2016 153 Tecopa 3 35.85141 -116.1867 700.2591 7
## 726 2016 153 Tecopa 3 35.85141 -116.1867 700.2591 7
## 727 2016 154 Tecopa 4 35.85134 -116.1866 700.8936 7
## 728 2016 154 Tecopa 4 35.85134 -116.1866 700.8936 7
## 729 2016 155 Tecopa 5 35.85130 -116.1866 700.1472 7
## 730 2016 155 Tecopa 5 35.85130 -116.1866 700.1472 7
## 731 2016 156 Tecopa 6 35.85127 -116.1866 699.6806 7
## 732 2016 156 Tecopa 6 35.85127 -116.1866 699.6806 7
## 733 2016 157 Tecopa 7 35.85121 -116.1866 700.7536 7
## 734 2016 157 Tecopa 7 35.85121 -116.1866 700.7536 7
## 735 2016 158 Tecopa 8 35.85118 -116.1865 700.9216 7
## 736 2016 158 Tecopa 8 35.85118 -116.1865 700.9216 7
## 737 2016 159 Tecopa 9 35.85117 -116.1864 702.2092 7
## 738 2016 159 Tecopa 9 35.85117 -116.1864 702.2092 7
## 739 2016 160 Tecopa 10 35.85127 -116.1863 702.9650 7
## 740 2016 160 Tecopa 10 35.85127 -116.1863 702.9650 7
## 741 2016 161 Tecopa 11 35.85123 -116.1862 702.8064 7
## 742 2016 161 Tecopa 11 35.85123 -116.1862 702.8064 7
## 743 2016 162 Tecopa 12 35.85118 -116.1863 704.1033 7
## 744 2016 162 Tecopa 12 35.85118 -116.1863 704.1033 7
## 745 2016 163 Tecopa 13 35.85115 -116.1864 704.3740 7
## 746 2016 163 Tecopa 13 35.85115 -116.1864 704.3740 7
## 747 2016 164 Tecopa 14 35.85108 -116.1865 705.4656 7
## 748 2016 164 Tecopa 14 35.85108 -116.1865 705.4656 7
## 749 2016 165 Tecopa 15 35.85108 -116.1866 705.0178 7
## 750 2016 165 Tecopa 15 35.85108 -116.1866 705.0178 7
## 751 2016 166 Tecopa 16 35.85102 -116.1866 706.5200 7
## 752 2016 166 Tecopa 16 35.85102 -116.1866 706.5200 7
## 753 2016 167 Tecopa 17 35.85116 -116.1867 706.6413 7
## 754 2016 167 Tecopa 17 35.85116 -116.1867 706.6413 7
## 755 2016 168 Tecopa 18 35.85123 -116.1867 707.5930 7
## 756 2016 168 Tecopa 18 35.85123 -116.1867 707.5930 7
## 757 2016 169 Tecopa 19 35.85124 -116.1868 707.6583 7
## 758 2016 169 Tecopa 19 35.85124 -116.1868 707.6583 7
## 759 2016 170 Tecopa 20 35.85130 -116.1869 445.7271 7
## 760 2016 170 Tecopa 20 35.85130 -116.1869 445.7271 7
## 761 2016 171 Tecopa 21 35.85133 -116.1868 446.2497 7
## 762 2016 171 Tecopa 21 35.85133 -116.1868 446.2497 7
## 763 2016 172 Tecopa 22 35.85140 -116.1869 447.1734 7
## 764 2016 172 Tecopa 22 35.85140 -116.1869 447.1734 7
## 765 2016 173 Tecopa 23 35.85145 -116.1869 447.4533 7
## 766 2016 173 Tecopa 23 35.85145 -116.1869 447.4533 7
## 767 2016 174 Tecopa 24 35.85146 -116.1868 448.7690 7
## 768 2016 174 Tecopa 24 35.85146 -116.1868 448.7690 7
## 769 2016 175 Tecopa 25 35.85150 -116.1868 448.7130 7
## 770 2016 175 Tecopa 25 35.85150 -116.1868 448.7130 7
## 771 2016 176 Tecopa 26 35.85152 -116.1869 449.0116 7
## 772 2016 176 Tecopa 26 35.85152 -116.1869 449.0116 7
## 773 2016 177 Tecopa 27 35.85147 -116.1869 447.8639 7
## 774 2016 177 Tecopa 27 35.85147 -116.1869 447.8639 7
## 775 2016 178 Tecopa 28 35.85153 -116.1870 449.0209 7
## 776 2016 178 Tecopa 28 35.85153 -116.1870 449.0209 7
## 777 2016 179 Tecopa 29 35.85168 -116.1870 448.9182 7
## 778 2016 179 Tecopa 29 35.85168 -116.1870 448.9182 7
## 779 2016 180 Tecopa 30 35.85101 -116.1872 449.0209 7
## 780 2016 180 Tecopa 30 35.85101 -116.1872 449.0209 7
## 781 2016 181 TejonRanch 1 34.87599 -118.6025 1118.0220 3
## 782 2016 181 TejonRanch 1 34.87599 -118.6025 1118.0220 3
## 783 2016 182 TejonRanch 2 34.87595 -118.6025 1118.0500 3
## 784 2016 182 TejonRanch 2 34.87595 -118.6025 1118.0500 3
## 785 2016 183 TejonRanch 3 34.87593 -118.6025 1117.6300 3
## 786 2016 183 TejonRanch 3 34.87593 -118.6025 1117.6300 3
## 787 2016 184 TejonRanch 4 34.87593 -118.6025 1117.5090 3
## 788 2016 184 TejonRanch 4 34.87593 -118.6025 1117.5090 3
## 789 2016 185 TejonRanch 5 34.87589 -118.6025 1117.4340 3
## 790 2016 185 TejonRanch 5 34.87589 -118.6025 1117.4340 3
## 791 2016 186 TejonRanch 6 34.87584 -118.6025 1117.3410 3
## 792 2016 186 TejonRanch 6 34.87584 -118.6025 1117.3410 3
## 793 2016 187 TejonRanch 7 34.87583 -118.6024 1117.2010 3
## 794 2016 187 TejonRanch 7 34.87583 -118.6024 1117.2010 3
## 795 2016 188 TejonRanch 8 34.87582 -118.6024 1116.5110 3
## 796 2016 188 TejonRanch 8 34.87582 -118.6024 1116.5110 3
## 797 2016 189 TejonRanch 9 34.87574 -118.6023 1115.8020 3
## 798 2016 189 TejonRanch 9 34.87574 -118.6023 1115.8020 3
## 799 2016 190 TejonRanch 10 34.87552 -118.6022 1114.5140 3
## 800 2016 190 TejonRanch 10 34.87552 -118.6022 1114.5140 3
## 801 2016 191 TejonRanch 11 34.87600 -118.6027 1116.3710 3
## 802 2016 191 TejonRanch 11 34.87600 -118.6027 1116.3710 3
## 803 2016 192 TejonRanch 12 34.87607 -118.6027 1116.9490 3
## 804 2016 192 TejonRanch 12 34.87607 -118.6027 1116.9490 3
## 805 2016 193 TejonRanch 13 34.87604 -118.6028 1117.4340 3
## 806 2016 193 TejonRanch 13 34.87604 -118.6028 1117.4340 3
## 807 2016 194 TejonRanch 14 34.87608 -118.6028 1117.6300 3
## 808 2016 194 TejonRanch 14 34.87608 -118.6028 1117.6300 3
## 809 2016 195 TejonRanch 15 34.87612 -118.6026 1117.1640 3
## 810 2016 195 TejonRanch 15 34.87612 -118.6026 1117.1640 3
## 811 2016 196 TejonRanch 16 34.87618 -118.6027 1117.7140 3
## 812 2016 196 TejonRanch 16 34.87618 -118.6027 1117.7140 3
## 813 2016 197 TejonRanch 17 34.87619 -118.6026 1117.7420 3
## 814 2016 197 TejonRanch 17 34.87619 -118.6026 1117.7420 3
## 815 2016 198 TejonRanch 18 34.87619 -118.6026 1118.2090 3
## 816 2016 198 TejonRanch 18 34.87619 -118.6026 1118.2090 3
## 817 2016 199 TejonRanch 19 34.87620 -118.6026 1118.0880 3
## 818 2016 199 TejonRanch 19 34.87620 -118.6026 1118.0880 3
## 819 2016 200 TejonRanch 20 34.87606 -118.6026 1117.5560 3
## 820 2016 200 TejonRanch 20 34.87606 -118.6026 1117.5560 3
## 821 2016 201 TejonRanch 21 34.87646 -118.6018 1117.7609 3
## 822 2016 201 TejonRanch 21 34.87646 -118.6018 1117.7609 3
## 823 2016 202 TejonRanch 22 34.87641 -118.6017 1116.6505 3
## 824 2016 202 TejonRanch 22 34.87641 -118.6017 1116.6505 3
## 825 2016 203 TejonRanch 23 34.87638 -118.6018 1117.1451 3
## 826 2016 203 TejonRanch 23 34.87638 -118.6018 1117.1451 3
## 827 2016 204 TejonRanch 24 34.87639 -118.6018 1117.3970 3
## 828 2016 204 TejonRanch 24 34.87639 -118.6018 1117.3970 3
## 829 2016 205 TejonRanch 25 34.87640 -118.6018 1117.1824 3
## 830 2016 205 TejonRanch 25 34.87640 -118.6018 1117.1824 3
## 831 2016 206 TejonRanch 26 34.87633 -118.6019 1115.8668 3
## 832 2016 206 TejonRanch 26 34.87633 -118.6019 1115.8668 3
## 833 2016 207 TejonRanch 27 34.87628 -118.6019 1116.7532 3
## 834 2016 207 TejonRanch 27 34.87628 -118.6019 1116.7532 3
## 835 2016 208 TejonRanch 28 34.87625 -118.6020 1116.4640 3
## 836 2016 208 TejonRanch 28 34.87625 -118.6020 1116.4640 3
## 837 2016 209 TejonRanch 29 34.87627 -118.6020 1116.5480 3
## 838 2016 209 TejonRanch 29 34.87627 -118.6020 1116.5480 3
## 839 2016 210 TejonRanch 30 34.87623 -118.6020 1117.2290 3
## 840 2016 210 TejonRanch 30 34.87623 -118.6020 1117.2290 3
## Microsite Abundance Richness Biomass B.rubens E.cicutarium
## 1 shrub 221 1 11.87950 221 0
## 2 open 175 3 1.99860 133 39
## 3 shrub 192 1 16.03870 192 0
## 4 open 161 4 5.64030 115 42
## 5 shrub 167 1 9.92170 167 0
## 6 open 142 2 2.02090 92 50
## 7 shrub 112 1 5.72630 112 0
## 8 open 143 3 3.28260 80 59
## 9 shrub 179 1 8.69420 179 0
## 10 open 162 3 3.22120 136 22
## 11 shrub 212 1 18.61760 212 0
## 12 open 146 3 3.55210 105 39
## 13 shrub 241 1 14.57810 241 0
## 14 open 141 3 5.38070 93 45
## 15 shrub 77 2 10.68060 73 4
## 16 open 129 2 3.63750 108 21
## 17 shrub 92 1 14.87970 92 0
## 18 open 131 2 3.12750 87 44
## 19 shrub 211 1 15.24860 211 0
## 20 open 205 3 4.96610 176 28
## 21 shrub 245 1 12.55180 245 0
## 22 open 185 2 5.86360 97 88
## 23 shrub 250 1 22.53730 250 0
## 24 open 163 3 7.35210 112 49
## 25 shrub 21 2 10.90710 9 12
## 26 open 170 2 4.29460 162 8
## 27 shrub 244 1 22.46610 244 0
## 28 open 128 3 2.91640 105 21
## 29 shrub 264 1 15.14000 264 0
## 30 open 131 4 9.43150 98 30
## 31 shrub 266 1 15.05400 266 0
## 32 open 116 3 6.90250 91 22
## 33 shrub 211 1 11.88910 211 0
## 34 open 169 2 6.70100 140 29
## 35 shrub 260 1 19.13290 260 0
## 36 open 143 2 1.39980 112 31
## 37 shrub 170 1 12.41270 170 0
## 38 open 136 2 2.37420 101 35
## 39 shrub 198 1 15.27590 198 0
## 40 open 148 2 2.08760 140 8
## 41 shrub 310 1 34.22070 310 0
## 42 open 129 3 2.87600 15 28
## 43 shrub 286 1 11.89440 286 0
## 44 open 117 3 14.68390 59 30
## 45 shrub 296 1 18.59040 296 0
## 46 open 140 3 4.32140 108 29
## 47 shrub 258 2 11.01730 256 0
## 48 open 165 4 13.20240 110 10
## 49 shrub 266 1 32.47440 266 0
## 50 open 134 2 7.64740 105 29
## 51 shrub 240 1 28.16850 240 0
## 52 open 186 2 3.88680 98 88
## 53 shrub 286 1 10.63740 286 0
## 54 open 172 3 3.50420 98 62
## 55 shrub 217 1 17.04760 217 0
## 56 open 200 3 4.71760 104 91
## 57 shrub 268 2 14.45670 263 0
## 58 open 137 2 2.36400 98 39
## 59 shrub 219 2 12.60390 214 5
## 60 open 116 2 4.86980 56 60
## 61 shrub 256 1 8.58120 256 0
## 62 open 91 5 1.65260 8 3
## 63 shrub 208 1 10.66130 208 0
## 64 open 132 2 1.11850 0 7
## 65 shrub 268 2 14.34510 243 0
## 66 open 103 3 3.21740 23 9
## 67 shrub 83 1 7.22370 83 0
## 68 open 87 3 0.88640 0 6
## 69 shrub 171 1 5.77210 171 0
## 70 open 116 4 6.87320 0 23
## 71 shrub 39 2 1.47820 28 11
## 72 open 83 3 1.16510 0 23
## 73 shrub 174 3 16.38720 163 3
## 74 open 107 3 0.94850 0 10
## 75 shrub 106 1 8.11320 106 0
## 76 open 52 4 0.25080 0 4
## 77 shrub 8 1 1.48020 8 0
## 78 open 68 3 0.19280 0 4
## 79 shrub 165 2 3.98620 163 0
## 80 open 102 3 1.00770 0 12
## 81 shrub 211 1 4.30930 211 0
## 82 open 109 3 1.82970 0 28
## 83 shrub 110 2 6.50120 108 0
## 84 open 118 3 2.18930 0 49
## 85 shrub 67 2 3.77440 63 4
## 86 open 82 3 1.39520 0 9
## 87 shrub 50 2 2.81670 48 2
## 88 open 62 3 1.05490 0 45
## 89 shrub 90 1 5.07010 90 0
## 90 open 77 3 1.31010 0 5
## 91 shrub 88 2 4.95740 83 0
## 92 open 50 3 0.85070 0 8
## 93 shrub 30 3 1.69000 11 9
## 94 open 42 3 0.71460 0 31
## 95 shrub 182 2 10.25280 165 0
## 96 open 49 3 0.83370 0 17
## 97 shrub 114 3 6.42210 107 0
## 98 open 141 3 2.39910 0 17
## 99 shrub 110 1 6.19670 110 0
## 100 open 160 4 2.72240 4 51
## 101 shrub 133 2 7.49240 112 0
## 102 open 95 4 1.61640 0 11
## 103 shrub 122 1 6.87270 122 0
## 104 open 119 4 2.02480 3 10
## 105 shrub 58 1 3.26740 58 0
## 106 open 55 3 0.93580 0 8
## 107 shrub 29 2 1.63370 26 0
## 108 open 62 4 1.05490 0 8
## 109 shrub 60 1 3.38000 60 0
## 110 open 71 2 1.20810 0 0
## 111 shrub 81 1 4.56300 81 0
## 112 open 88 3 1.49730 0 3
## 113 shrub 53 1 2.98570 53 0
## 114 open 76 3 1.29310 0 4
## 115 shrub 35 3 1.97170 31 1
## 116 open 96 3 1.63340 0 4
## 117 shrub 115 1 6.47840 115 0
## 118 open 63 4 1.07190 8 6
## 119 shrub 117 2 6.59110 115 0
## 120 open 63 3 1.07190 0 8
## 121 shrub 20 2 1.83530 0 0
## 122 open 23 2 0.15060 0 0
## 123 shrub 66 3 0.74410 0 0
## 124 open 13 1 0.08190 0 0
## 125 shrub 23 3 2.71840 0 0
## 126 open 39 1 0.27830 0 0
## 127 shrub 21 2 1.82420 0 0
## 128 open 9 3 0.21370 0 0
## 129 shrub 11 3 1.66470 0 0
## 130 open 21 1 0.66770 0 0
## 131 shrub 9 1 1.78140 0 0
## 132 open 10 2 0.58200 0 0
## 133 shrub 6 2 1.77240 0 0
## 134 open 13 2 0.74630 0 0
## 135 shrub 13 2 0.50750 0 0
## 136 open 14 1 0.10040 0 0
## 137 shrub 22 3 2.24130 0 0
## 138 open 14 2 0.87430 0 0
## 139 shrub 16 1 0.52610 0 0
## 140 open 23 1 0.30270 0 0
## 141 shrub 21 3 1.81080 0 0
## 142 open 11 3 0.67980 0 0
## 143 shrub 17 1 0.61790 0 0
## 144 open 18 3 0.45470 0 0
## 145 shrub 3 2 0.50040 0 0
## 146 open 24 2 0.14130 0 0
## 147 shrub 14 1 2.93890 0 0
## 148 open 12 1 0.20780 0 0
## 149 shrub 8 2 4.20920 0 0
## 150 open 14 3 0.70520 0 3
## 151 shrub 17 2 0.75770 0 0
## 152 open 23 2 0.22760 0 0
## 153 shrub 14 1 4.85360 0 0
## 154 open 18 3 0.15070 0 2
## 155 shrub 14 2 0.70310 0 0
## 156 open 15 1 0.26790 0 0
## 157 shrub 11 3 1.30160 0 0
## 158 open 15 2 0.56570 0 0
## 159 shrub 13 4 1.30640 0 0
## 160 open 18 3 0.31180 0 6
## 161 shrub 23 1 1.71220 0 0
## 162 open 24 2 1.10560 0 0
## 163 shrub 20 3 2.85180 0 3
## 164 open 10 3 0.34660 0 2
## 165 shrub 14 3 1.49880 0 0
## 166 open 15 3 0.31810 0 0
## 167 shrub 16 3 2.00740 0 3
## 168 open 12 2 0.17430 0 0
## 169 shrub 11 3 1.15440 0 0
## 170 open 31 4 0.54730 0 3
## 171 shrub 18 2 1.02320 0 0
## 172 open 21 1 0.21560 0 0
## 173 shrub 20 2 0.87010 0 0
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## 840 0 0aggregate(Richness~1, FUN=mean, data=community)## Richness
## 1 2.228571